Zaawansowana Esploracja Danych - Raport

grudzień 11, 2015

0. Analiza wyników

Analizując badany zbiór danych opisujący poszczególne ligandy, można było zauważyć wiele zależności między poszczególnymi zmiennymi opisujących poszczególne wystąpienia ligandu. Niektóre zmienne, przechowywały wartości stałe, które nie niosły ze sobą wartościowej informacji w eksploracji danych, inne zmienne opisywały wyliczone wcześniej atrybuty, bądź odnosicły się do poprawności pliku PDB. Jednym z postawionych wniosków było to, że niektóre zmienne były ze sobą silnie powiązane, niektóre zmienne opisywały tą samą informację, różniąc sie jedynie progiem odcięcia. Analizując dane można było także stworzyć pewne modele klasyfikacyjne, a także modele regresji, które ułatwiały predykcję innych wartości.

1. Kod wyliczający wykorzystane biblioteki.

library(knitr)
library(dplyr)
library(ggplot2)
library(tidyr)
library(ggExtra)
library(corrgram)
library(RColorBrewer)
library(caret)
library(pROC)
opts_chunk$set(echo=TRUE, cache=TRUE)

---

Powyżej znajduje się lista wykorzystanych bibliotek.

Dodatkowo ustawione zostały zmienne globalne, które mają zgeneralizować wyświetlanie fragmentów kodu i utrzymywaniu poszczególnych fragmentów w pamięci.

---

2. Kod zapewniający powtarzalność wyników przy każdym uruchomieniu raportu na tych samych danych.

set.seed(23)

---

Powtarzalność badań zapeniła funkcja set.seed() z odpowiednio ustawionym ziarnem. ***

3. Kod pozwalający wczytać dane z pliku.

dane <- read.table(file = "all_summary.txt", 
                   header = FALSE,
                   sep = ";",
                   dec = ".",
                   fill = FALSE, #czy usupełniać kolumny
                   col.names = strsplit(readLines("all_summary.txt", n = 1), ";")[[1]], 
                   skip = 1,
                   na.strings = c('NAN'),
                   blank.lines.skip = TRUE, #Opóść linie puste
                   stringsAsFactors = TRUE
                   )

---

Do wczytania danych wykorzystana została funkcja read.table(). By dokonać dokładnego odwzorowania danych, znajdujących się w pliku tekstowym, do obiektu data.frame, ustawione zostały odpowiednie argumenty funkcji. ***

4. Kod usuwający z danych wiersze posiadające wartość zmiennej res_name równą: “DA”,“DC”,“DT”, “DU”, “DG”, “DI”,“UNK”, “UNX”, “UNL”, “PR”, “PD”, “Y1”, “EU”, “N”, “15P”, “UQ”, “PX4” lub “NAN”.

dane <- dane %>% 
  filter(!is.na(res_name), 
         !is.nan(res_name),
         !res_name %in% c('DA','DC','DT','DU','DG','DI','UNK','UNX','UNL','PR','PD','Y1','EU','N','15P','UQ','PX4','NAN')) 

---

Jako, że zmienna res_name w późniejszych badaniach posłużyła jakozmienna klasyfikująca dany ligard, musiała ona być odpowiednio identyfikowana przez określoną wartość. Mając to na uwadze, usunięte zostały wiersze, których wartość zmiennej res_name była niedostępna, czyli równa NA. ***

5. Kod pozostawiający tylko unikatowe pary wartości (pdb_code, res_name).

dane <- dane %>% 
  distinct(pdb_code, res_name)

6. Krótkie podsumowanie wartości w każdej kolumnie.

Tabela przedstawiająca dane po wstępnym czyszczeniu

kable(summary(dane))

	title	pdb_code	res_name	res_id	chain_id	local_BAa	local_NPa	local_Ra	local_RGa	local_SRGa	local_CCSa	local_CCPa	local_ZOa	local_ZDa	local_ZD_minus_a	local_ZD_plus_a	local_res_atom_count	local_res_atom_non_h_count	local_res_atom_non_h_occupancy_sum	local_res_atom_non_h_electron_sum	local_res_atom_non_h_electron_occupancy_sum	local_res_atom_C_count	local_res_atom_N_count	local_res_atom_O_count	local_res_atom_S_count	dict_atom_non_h_count	dict_atom_non_h_electron_sum	dict_atom_C_count	dict_atom_N_count	dict_atom_O_count	dict_atom_S_count	part_00_blob_electron_sum	part_00_blob_volume_sum	part_00_blob_parts	part_00_shape_O3	part_00_shape_O4	part_00_shape_O5	part_00_shape_FL	part_00_shape_O3_norm	part_00_shape_O4_norm	part_00_shape_O5_norm	part_00_shape_FL_norm	part_00_shape_I1	part_00_shape_I2	part_00_shape_I3	part_00_shape_I4	part_00_shape_I5	part_00_shape_I6	part_00_shape_I1_norm	part_00_shape_I2_norm	part_00_shape_I3_norm	part_00_shape_I4_norm	part_00_shape_I5_norm	part_00_shape_I6_norm	part_00_shape_I1_scaled	part_00_shape_I2_scaled	part_00_shape_I3_scaled	part_00_shape_I4_scaled	part_00_shape_I5_scaled	part_00_shape_I6_scaled	part_00_shape_M000	part_00_shape_E3_E1	part_00_shape_E2_E1	part_00_shape_E3_E2	part_00_shape_sqrt_E1	part_00_shape_sqrt_E2	part_00_shape_sqrt_E3	part_00_density_O3	part_00_density_O4	part_00_density_O5	part_00_density_FL	part_00_density_O3_norm	part_00_density_O4_norm	part_00_density_O5_norm	part_00_density_FL_norm	part_00_density_I1	part_00_density_I2	part_00_density_I3	part_00_density_I4	part_00_density_I5	part_00_density_I6	part_00_density_I1_norm	part_00_density_I2_norm	part_00_density_I3_norm	part_00_density_I4_norm	part_00_density_I5_norm	part_00_density_I6_norm	part_00_density_I1_scaled	part_00_density_I2_scaled	part_00_density_I3_scaled	part_00_density_I4_scaled	part_00_density_I5_scaled	part_00_density_I6_scaled	part_00_density_M000	part_00_density_E3_E1	part_00_density_E2_E1	part_00_density_E3_E2	part_00_density_sqrt_E1	part_00_density_sqrt_E2	part_00_density_sqrt_E3	part_01_blob_electron_sum	part_01_blob_volume_sum	part_01_blob_parts	part_01_shape_O3	part_01_shape_O4	part_01_shape_O5	part_01_shape_FL	part_01_shape_O3_norm	part_01_shape_O4_norm	part_01_shape_O5_norm	part_01_shape_FL_norm	part_01_shape_I1	part_01_shape_I2	part_01_shape_I3	part_01_shape_I4	part_01_shape_I5	part_01_shape_I6	part_01_shape_I1_norm	part_01_shape_I2_norm	part_01_shape_I3_norm	part_01_shape_I4_norm	part_01_shape_I5_norm	part_01_shape_I6_norm	part_01_shape_I1_scaled	part_01_shape_I2_scaled	part_01_shape_I3_scaled	part_01_shape_I4_scaled	part_01_shape_I5_scaled	part_01_shape_I6_scaled	part_01_shape_M000	part_01_shape_E3_E1	part_01_shape_E2_E1	part_01_shape_E3_E2	part_01_shape_sqrt_E1	part_01_shape_sqrt_E2	part_01_shape_sqrt_E3	part_01_density_O3	part_01_density_O4	part_01_density_O5	part_01_density_FL	part_01_density_O3_norm	part_01_density_O4_norm	part_01_density_O5_norm	part_01_density_FL_norm	part_01_density_I1	part_01_density_I2	part_01_density_I3	part_01_density_I4	part_01_density_I5	part_01_density_I6	part_01_density_I1_norm	part_01_density_I2_norm	part_01_density_I3_norm	part_01_density_I4_norm	part_01_density_I5_norm	part_01_density_I6_norm	part_01_density_I1_scaled	part_01_density_I2_scaled	part_01_density_I3_scaled	part_01_density_I4_scaled	part_01_density_I5_scaled	part_01_density_I6_scaled	part_01_density_M000	part_01_density_E3_E1	part_01_density_E2_E1	part_01_density_E3_E2	part_01_density_sqrt_E1	part_01_density_sqrt_E2	part_01_density_sqrt_E3	part_02_blob_electron_sum	part_02_blob_volume_sum	part_02_blob_parts	part_02_shape_O3	part_02_shape_O4	part_02_shape_O5	part_02_shape_FL	part_02_shape_O3_norm	part_02_shape_O4_norm	part_02_shape_O5_norm	part_02_shape_FL_norm	part_02_shape_I1	part_02_shape_I2	part_02_shape_I3	part_02_shape_I4	part_02_shape_I5	part_02_shape_I6	part_02_shape_I1_norm	part_02_shape_I2_norm	part_02_shape_I3_norm	part_02_shape_I4_norm	part_02_shape_I5_norm	part_02_shape_I6_norm	part_02_shape_I1_scaled	part_02_shape_I2_scaled	part_02_shape_I3_scaled	part_02_shape_I4_scaled	part_02_shape_I5_scaled	part_02_shape_I6_scaled	part_02_shape_M000	part_02_shape_E3_E1	part_02_shape_E2_E1	part_02_shape_E3_E2	part_02_shape_sqrt_E1	part_02_shape_sqrt_E2	part_02_shape_sqrt_E3	part_02_density_O3	part_02_density_O4	part_02_density_O5	part_02_density_FL	part_02_density_O3_norm	part_02_density_O4_norm	part_02_density_O5_norm	part_02_density_FL_norm	part_02_density_I1	part_02_density_I2	part_02_density_I3	part_02_density_I4	part_02_density_I5	part_02_density_I6	part_02_density_I1_norm	part_02_density_I2_norm	part_02_density_I3_norm	part_02_density_I4_norm	part_02_density_I5_norm	part_02_density_I6_norm	part_02_density_I1_scaled	part_02_density_I2_scaled	part_02_density_I3_scaled	part_02_density_I4_scaled	part_02_density_I5_scaled	part_02_density_I6_scaled	part_02_density_M000	part_02_density_E3_E1	part_02_density_E2_E1	part_02_density_E3_E2	part_02_density_sqrt_E1	part_02_density_sqrt_E2	part_02_density_sqrt_E3	part_03_blob_electron_sum	part_03_blob_volume_sum	part_03_blob_parts	part_03_shape_O3	part_03_shape_O4	part_03_shape_O5	part_03_shape_FL	part_03_shape_O3_norm	part_03_shape_O4_norm	part_03_shape_O5_norm	part_03_shape_FL_norm	part_03_shape_I1	part_03_shape_I2	part_03_shape_I3	part_03_shape_I4	part_03_shape_I5	part_03_shape_I6	part_03_shape_I1_norm	part_03_shape_I2_norm	part_03_shape_I3_norm	part_03_shape_I4_norm	part_03_shape_I5_norm	part_03_shape_I6_norm	part_03_shape_I1_scaled	part_03_shape_I2_scaled	part_03_shape_I3_scaled	part_03_shape_I4_scaled	part_03_shape_I5_scaled	part_03_shape_I6_scaled	part_03_shape_M000	part_03_shape_E3_E1	part_03_shape_E2_E1	part_03_shape_E3_E2	part_03_shape_sqrt_E1	part_03_shape_sqrt_E2	part_03_shape_sqrt_E3	part_03_density_O3	part_03_density_O4	part_03_density_O5	part_03_density_FL	part_03_density_O3_norm	part_03_density_O4_norm	part_03_density_O5_norm	part_03_density_FL_norm	part_03_density_I1	part_03_density_I2	part_03_density_I3	part_03_density_I4	part_03_density_I5	part_03_density_I6	part_03_density_I1_norm	part_03_density_I2_norm	part_03_density_I3_norm	part_03_density_I4_norm	part_03_density_I5_norm	part_03_density_I6_norm	part_03_density_I1_scaled	part_03_density_I2_scaled	part_03_density_I3_scaled	part_03_density_I4_scaled	part_03_density_I5_scaled	part_03_density_I6_scaled	part_03_density_M000	part_03_density_E3_E1	part_03_density_E2_E1	part_03_density_E3_E2	part_03_density_sqrt_E1	part_03_density_sqrt_E2	part_03_density_sqrt_E3	part_04_blob_electron_sum	part_04_blob_volume_sum	part_04_blob_parts	part_04_shape_O3	part_04_shape_O4	part_04_shape_O5	part_04_shape_FL	part_04_shape_O3_norm	part_04_shape_O4_norm	part_04_shape_O5_norm	part_04_shape_FL_norm	part_04_shape_I1	part_04_shape_I2	part_04_shape_I3	part_04_shape_I4	part_04_shape_I5	part_04_shape_I6	part_04_shape_I1_norm	part_04_shape_I2_norm	part_04_shape_I3_norm	part_04_shape_I4_norm	part_04_shape_I5_norm	part_04_shape_I6_norm	part_04_shape_I1_scaled	part_04_shape_I2_scaled	part_04_shape_I3_scaled	part_04_shape_I4_scaled	part_04_shape_I5_scaled	part_04_shape_I6_scaled	part_04_shape_M000	part_04_shape_E3_E1	part_04_shape_E2_E1	part_04_shape_E3_E2	part_04_shape_sqrt_E1	part_04_shape_sqrt_E2	part_04_shape_sqrt_E3	part_04_density_O3	part_04_density_O4	part_04_density_O5	part_04_density_FL	part_04_density_O3_norm	part_04_density_O4_norm	part_04_density_O5_norm	part_04_density_FL_norm	part_04_density_I1	part_04_density_I2	part_04_density_I3	part_04_density_I4	part_04_density_I5	part_04_density_I6	part_04_density_I1_norm	part_04_density_I2_norm	part_04_density_I3_norm	part_04_density_I4_norm	part_04_density_I5_norm	part_04_density_I6_norm	part_04_density_I1_scaled	part_04_density_I2_scaled	part_04_density_I3_scaled	part_04_density_I4_scaled	part_04_density_I5_scaled	part_04_density_I6_scaled	part_04_density_M000	part_04_density_E3_E1	part_04_density_E2_E1	part_04_density_E3_E2	part_04_density_sqrt_E1	part_04_density_sqrt_E2	part_04_density_sqrt_E3	part_05_blob_electron_sum	part_05_blob_volume_sum	part_05_blob_parts	part_05_shape_O3	part_05_shape_O4	part_05_shape_O5	part_05_shape_FL	part_05_shape_O3_norm	part_05_shape_O4_norm	part_05_shape_O5_norm	part_05_shape_FL_norm	part_05_shape_I1	part_05_shape_I2	part_05_shape_I3	part_05_shape_I4	part_05_shape_I5	part_05_shape_I6	part_05_shape_I1_norm	part_05_shape_I2_norm	part_05_shape_I3_norm	part_05_shape_I4_norm	part_05_shape_I5_norm	part_05_shape_I6_norm	part_05_shape_I1_scaled	part_05_shape_I2_scaled	part_05_shape_I3_scaled	part_05_shape_I4_scaled	part_05_shape_I5_scaled	part_05_shape_I6_scaled	part_05_shape_M000	part_05_shape_E3_E1	part_05_shape_E2_E1	part_05_shape_E3_E2	part_05_shape_sqrt_E1	part_05_shape_sqrt_E2	part_05_shape_sqrt_E3	part_05_density_O3	part_05_density_O4	part_05_density_O5	part_05_density_FL	part_05_density_O3_norm	part_05_density_O4_norm	part_05_density_O5_norm	part_05_density_FL_norm	part_05_density_I1	part_05_density_I2	part_05_density_I3	part_05_density_I4	part_05_density_I5	part_05_density_I6	part_05_density_I1_norm	part_05_density_I2_norm	part_05_density_I3_norm	part_05_density_I4_norm	part_05_density_I5_norm	part_05_density_I6_norm	part_05_density_I1_scaled	part_05_density_I2_scaled	part_05_density_I3_scaled	part_05_density_I4_scaled	part_05_density_I5_scaled	part_05_density_I6_scaled	part_05_density_M000	part_05_density_E3_E1	part_05_density_E2_E1	part_05_density_E3_E2	part_05_density_sqrt_E1	part_05_density_sqrt_E2	part_05_density_sqrt_E3	part_06_blob_electron_sum	part_06_blob_volume_sum	part_06_blob_parts	part_06_shape_O3	part_06_shape_O4	part_06_shape_O5	part_06_shape_FL	part_06_shape_O3_norm	part_06_shape_O4_norm	part_06_shape_O5_norm	part_06_shape_FL_norm	part_06_shape_I1	part_06_shape_I2	part_06_shape_I3	part_06_shape_I4	part_06_shape_I5	part_06_shape_I6	part_06_shape_I1_norm	part_06_shape_I2_norm	part_06_shape_I3_norm	part_06_shape_I4_norm	part_06_shape_I5_norm	part_06_shape_I6_norm	part_06_shape_I1_scaled	part_06_shape_I2_scaled	part_06_shape_I3_scaled	part_06_shape_I4_scaled	part_06_shape_I5_scaled	part_06_shape_I6_scaled	part_06_shape_M000	part_06_shape_E3_E1	part_06_shape_E2_E1	part_06_shape_E3_E2	part_06_shape_sqrt_E1	part_06_shape_sqrt_E2	part_06_shape_sqrt_E3	part_06_density_O3	part_06_density_O4	part_06_density_O5	part_06_density_FL	part_06_density_O3_norm	part_06_density_O4_norm	part_06_density_O5_norm	part_06_density_FL_norm	part_06_density_I1	part_06_density_I2	part_06_density_I3	part_06_density_I4	part_06_density_I5	part_06_density_I6	part_06_density_I1_norm	part_06_density_I2_norm	part_06_density_I3_norm	part_06_density_I4_norm	part_06_density_I5_norm	part_06_density_I6_norm	part_06_density_I1_scaled	part_06_density_I2_scaled	part_06_density_I3_scaled	part_06_density_I4_scaled	part_06_density_I5_scaled	part_06_density_I6_scaled	part_06_density_M000	part_06_density_E3_E1	part_06_density_E2_E1	part_06_density_E3_E2	part_06_density_sqrt_E1	part_06_density_sqrt_E2	part_06_density_sqrt_E3	part_07_blob_electron_sum	part_07_blob_volume_sum	part_07_blob_parts	part_07_shape_O3	part_07_shape_O4	part_07_shape_O5	part_07_shape_FL	part_07_shape_O3_norm	part_07_shape_O4_norm	part_07_shape_O5_norm	part_07_shape_FL_norm	part_07_shape_I1	part_07_shape_I2	part_07_shape_I3	part_07_shape_I4	part_07_shape_I5	part_07_shape_I6	part_07_shape_I1_norm	part_07_shape_I2_norm	part_07_shape_I3_norm	part_07_shape_I4_norm	part_07_shape_I5_norm	part_07_shape_I6_norm	part_07_shape_I1_scaled	part_07_shape_I2_scaled	part_07_shape_I3_scaled	part_07_shape_I4_scaled	part_07_shape_I5_scaled	part_07_shape_I6_scaled	part_07_shape_M000	part_07_shape_E3_E1	part_07_shape_E2_E1	part_07_shape_E3_E2	part_07_shape_sqrt_E1	part_07_shape_sqrt_E2	part_07_shape_sqrt_E3	part_07_density_O3	part_07_density_O4	part_07_density_O5	part_07_density_FL	part_07_density_O3_norm	part_07_density_O4_norm	part_07_density_O5_norm	part_07_density_FL_norm	part_07_density_I1	part_07_density_I2	part_07_density_I3	part_07_density_I4	part_07_density_I5	part_07_density_I6	part_07_density_I1_norm	part_07_density_I2_norm	part_07_density_I3_norm	part_07_density_I4_norm	part_07_density_I5_norm	part_07_density_I6_norm	part_07_density_I1_scaled	part_07_density_I2_scaled	part_07_density_I3_scaled	part_07_density_I4_scaled	part_07_density_I5_scaled	part_07_density_I6_scaled	part_07_density_M000	part_07_density_E3_E1	part_07_density_E2_E1	part_07_density_E3_E2	part_07_density_sqrt_E1	part_07_density_sqrt_E2	part_07_density_sqrt_E3	part_08_blob_electron_sum	part_08_blob_volume_sum	part_08_blob_parts	part_08_shape_O3	part_08_shape_O4	part_08_shape_O5	part_08_shape_FL	part_08_shape_O3_norm	part_08_shape_O4_norm	part_08_shape_O5_norm	part_08_shape_FL_norm	part_08_shape_I1	part_08_shape_I2	part_08_shape_I3	part_08_shape_I4	part_08_shape_I5	part_08_shape_I6	part_08_shape_I1_norm	part_08_shape_I2_norm	part_08_shape_I3_norm	part_08_shape_I4_norm	part_08_shape_I5_norm	part_08_shape_I6_norm	part_08_shape_I1_scaled	part_08_shape_I2_scaled	part_08_shape_I3_scaled	part_08_shape_I4_scaled	part_08_shape_I5_scaled	part_08_shape_I6_scaled	part_08_shape_M000	part_08_shape_E3_E1	part_08_shape_E2_E1	part_08_shape_E3_E2	part_08_shape_sqrt_E1	part_08_shape_sqrt_E2	part_08_shape_sqrt_E3	part_08_density_O3	part_08_density_O4	part_08_density_O5	part_08_density_FL	part_08_density_O3_norm	part_08_density_O4_norm	part_08_density_O5_norm	part_08_density_FL_norm	part_08_density_I1	part_08_density_I2	part_08_density_I3	part_08_density_I4	part_08_density_I5	part_08_density_I6	part_08_density_I1_norm	part_08_density_I2_norm	part_08_density_I3_norm	part_08_density_I4_norm	part_08_density_I5_norm	part_08_density_I6_norm	part_08_density_I1_scaled	part_08_density_I2_scaled	part_08_density_I3_scaled	part_08_density_I4_scaled	part_08_density_I5_scaled	part_08_density_I6_scaled	part_08_density_M000	part_08_density_E3_E1	part_08_density_E2_E1	part_08_density_E3_E2	part_08_density_sqrt_E1	part_08_density_sqrt_E2	part_08_density_sqrt_E3	part_09_blob_electron_sum	part_09_blob_volume_sum	part_09_blob_parts	part_09_shape_O3	part_09_shape_O4	part_09_shape_O5	part_09_shape_FL	part_09_shape_O3_norm	part_09_shape_O4_norm	part_09_shape_O5_norm	part_09_shape_FL_norm	part_09_shape_I1	part_09_shape_I2	part_09_shape_I3	part_09_shape_I4	part_09_shape_I5	part_09_shape_I6	part_09_shape_I1_norm	part_09_shape_I2_norm	part_09_shape_I3_norm	part_09_shape_I4_norm	part_09_shape_I5_norm	part_09_shape_I6_norm	part_09_shape_I1_scaled	part_09_shape_I2_scaled	part_09_shape_I3_scaled	part_09_shape_I4_scaled	part_09_shape_I5_scaled	part_09_shape_I6_scaled	part_09_shape_M000	part_09_shape_E3_E1	part_09_shape_E2_E1	part_09_shape_E3_E2	part_09_shape_sqrt_E1	part_09_shape_sqrt_E2	part_09_shape_sqrt_E3	part_09_density_O3	part_09_density_O4	part_09_density_O5	part_09_density_FL	part_09_density_O3_norm	part_09_density_O4_norm	part_09_density_O5_norm	part_09_density_FL_norm	part_09_density_I1	part_09_density_I2	part_09_density_I3	part_09_density_I4	part_09_density_I5	part_09_density_I6	part_09_density_I1_norm	part_09_density_I2_norm	part_09_density_I3_norm	part_09_density_I4_norm	part_09_density_I5_norm	part_09_density_I6_norm	part_09_density_I1_scaled	part_09_density_I2_scaled	part_09_density_I3_scaled	part_09_density_I4_scaled	part_09_density_I5_scaled	part_09_density_I6_scaled	part_09_density_M000	part_09_density_E3_E1	part_09_density_E2_E1	part_09_density_E3_E2	part_09_density_sqrt_E1	part_09_density_sqrt_E2	part_09_density_sqrt_E3	local_volume	local_electrons	local_mean	local_std	local_min	local_max	local_skewness	local_parts	fo_col	fc_col	weight_col	grid_space	solvent_radius	solvent_opening_radius	resolution_max_limit	resolution	TwoFoFc_mean	TwoFoFc_std	TwoFoFc_square_std	TwoFoFc_min	TwoFoFc_max	Fo_mean	Fo_std	Fo_square_std	Fo_min	Fo_max	FoFc_mean	FoFc_std	FoFc_square_std	FoFc_min	FoFc_max	Fc_mean	Fc_std	Fc_square_std	Fc_min	Fc_max	solvent_mask_count	void_mask_count	modeled_mask_count	solvent_ratio	TwoFoFc_bulk_mean	TwoFoFc_bulk_std	TwoFoFc_void_mean	TwoFoFc_void_std	TwoFoFc_modeled_mean	TwoFoFc_modeled_std	Fo_bulk_mean	Fo_bulk_std	Fo_void_mean	Fo_void_std	Fo_modeled_mean	Fo_modeled_std	FoFc_bulk_mean	FoFc_bulk_std	FoFc_void_mean	FoFc_void_std	FoFc_modeled_mean	FoFc_modeled_std	Fc_bulk_mean	Fc_bulk_std	Fc_void_mean	Fc_void_std	Fc_modeled_mean	Fc_modeled_std	TwoFoFc_void_fit_binormal_mean1	TwoFoFc_void_fit_binormal_std1	TwoFoFc_void_fit_binormal_mean2	TwoFoFc_void_fit_binormal_std2	TwoFoFc_void_fit_binormal_scale	TwoFoFc_solvent_fit_normal_mean	TwoFoFc_solvent_fit_normal_std	part_step_FoFc_std_min	part_step_FoFc_std_max	part_step_FoFc_std_step
	110l BME 901 A: 1	3ag4 : 16	SO4 :1183	301 : 430	A :8630	Min. : NA	Min. : NA	Min. : NA	Min. : NA	Min. : NA	Min. : NA	Min. : NA	Min. : NA	Min. : NA	Min. : NA	Min. : NA	Min. : 1.00	Min. : 1.00	Min. : 0.00	Min. : 3.0	Min. : 0.00	Min. : 0.000	Min. : 0.00	Min. : 0.000	Min. :0.0000	Min. : 1.00	Min. : 3.0	Min. : 0.00	Min. : 0.000	Min. : 0.000	Min. :0.0000	Min. : 0.000	Min. : 0.00	Min. :0.0000	Min. : 24253	Min. :1.952e+08	Min. :5.160e+11	Min. :3.330e+06	Min. :0.2312	Min. :0.0178	Min. :0.0005	Min. :0.0000	Min. :6.875e+05	Min. :1.246e+11	Min. :9.688e+10	Min. :1.335e+06	Min. :4.728e+03	Min. :5.618e+09	Min. : 0.0637	Min. : 0.0011	Min. : 0.0008	Min. :0.0000	Min. :0.0000	Min. : 0.0049	Min. :0.0001	Min. :0.0000	Min. :0.0000	Min. :0.0000	Min. :0e+00	Min. :0	Min. : 1023	Min. :0.0005	Min. :0.0142	Min. :0.0042	Min. : 2.930	Min. : 1.793	Min. : 0.498	Min. : 1527	Min. :7.096e+05	Min. :1.011e+08	Min. :9.595e+05	Min. :0.0593	Min. :0.0012	Min. :0.0000	Min. : 0.0000	Min. :6.548e+04	Min. :9.611e+08	Min. :1.275e+09	Min. :4.130e+05	Min. :2.366e+04	Min. :3.899e+07	Min. : 0.0048	Min. : 0.0000	Min. : 0.0000	Min. : 0.0000	Min. : 0.0000	Min. : 0.0001	Min. :0.0000	Min. :0.0000	Min. :0.0000	Min. :0.0000	Min. :0.0000	Min. :0.0000	Min. : 47	Min. :0.0007	Min. :0.0133	Min. :0.0052	Min. : 2.499	Min. : 1.769	Min. : 0.4972	Min. : 0.000	Min. : 0.00	Min. :0.0000	Min. : 24186	Min. :1.947e+08	Min. :5.217e+11	Min. :2.023e+06	Min. :0.2313	Min. :0.0178	Min. :0.0005	Min. :0.0000	Min. :6.807e+05	Min. :1.231e+11	Min. :9.354e+10	Min. :8.184e+05	Min. :3.360e+03	Min. :5.502e+09	Min. : 0.0637	Min. : 0.0011	Min. : 0.0008	Min. :0.0000	Min. :0.0000	Min. : 0.0049	Min. :0.0001	Min. :0.0000	Min. :0.000	Min. :0.0000	Min. :0.000	Min. :0	Min. : 1023	Min. :0.0005	Min. :0.0127	Min. :0.0044	Min. : 2.898	Min. : 1.947	Min. : 0.4978	Min. : 1038	Min. :3.280e+05	Min. :3.182e+07	Min. :3.230e+05	Min. :0.0592	Min. :0.0012	Min. :0.0000	Min. : 0.0000	Min. :3.748e+04	Min. :3.151e+08	Min. :4.195e+08	Min. :1.414e+05	Min. :1.786e+04	Min. :1.512e+07	Min. : 0.0048	Min. : 0.0000	Min. : 0.000	Min. : 0.0000	Min. : 0.0000	Min. : 0.0001	Min. :0.0000	Min. :0.0000	Min. :0.0000	Min. :0.0000	Min. :0.0000	Min. :0.0000	Min. : 37.76	Min. :0.0007	Min. :0.0122	Min. :0.0054	Min. : 2.496	Min. : 1.877	Min. : 0.4971	Min. : 0.00	Min. : 0.000	Min. :0.0000	Min. : 24331	Min. :1.955e+08	Min. :5.180e+11	Min. :2.142e+06	Min. :0.2312	Min. :0.0178	Min. :0.0005	Min. : 0.0000	Min. :6.937e+05	Min. :1.254e+11	Min. :1.013e+11	Min. :8.662e+05	Min. :3.121e+03	Min. :5.751e+09	Min. : 0.0637	Min. : 0.0011	Min. : 0.0008	Min. : 0.0000	Min. : 0.0000	Min. : 0.0049	Min. :0.0001	Min. :0.0000	Min. :0.0000	Min. :0.0000	Min. :0.0000	Min. :0	Min. : 1023	Min. :0.0006	Min. :0.0138	Min. :0.0047	Min. : 2.917	Min. : 1.914	Min. : 0.4957	Min. : 4269	Min. :5.654e+06	Min. :2.385e+09	Min. :1.443e+06	Min. :0.0588	Min. :0.0011	Min. :0.0000	Min. : 0.0000	Min. :1.324e+05	Min. :3.851e+09	Min. :4.897e+09	Min. :6.988e+05	Min. :1.294e+04	Min. :2.080e+08	Min. : 0.0047	Min. : 0.0000	Min. : 0.000	Min. : 0.0000	Min. : 0.0000	Min. : 0.0001	Min. :0.0000	Min. :0.0000	Min. :0.0000	Min. :0.0000	Min. :0.0000	Min. :0.0000	Min. : 177.5	Min. :0.0008	Min. :0.0138	Min. :0.0058	Min. : 2.487	Min. : 1.857	Min. : 0.4957	Min. : 0.0	Min. : 0.00	Min. :0.0000	Min. : 24164	Min. :1.941e+08	Min. :5.143e+11	Min. :6.027e+06	Min. :0.231	Min. :0.018	Min. :0.000	Min. : 0.000	Min. :6.790e+05	Min. :1.225e+11	Min. :9.315e+10	Min. :2.433e+06	Min. :1.301e+03	Min. :5.484e+09	Min. : 0.064	Min. : 0.001	Min. : 0.001	Min. : 0.000	Min. : 0.000	Min. : 0.005	Min. :0.000	Min. :0.000	Min. :0.00	Min. :0.000	Min. :0.000	Min. :0	Min. : 1023	Min. :0.001	Min. :0.012	Min. :0.005	Min. : 2.884	Min. : 1.817	Min. : 0.493	Min. : 4027	Min. :5.262e+06	Min. :2.243e+09	Min. :1.389e+06	Min. : 0.058	Min. : 0.001	Min. :0.000	Min. : 0.000	Min. :1.186e+05	Min. :3.489e+09	Min. :3.239e+09	Min. :5.853e+05	Min. :1.301e+04	Min. :1.669e+08	Min. : 0.005	Min. : 0.000	Min. : 0.00	Min. : 0.000	Min. : 0.000	Min. : 0.000	Min. :0.000	Min. :0.000	Min. : 0.000	Min. :0.00	Min. :0.000	Min. :0.000	Min. : 168	Min. :0.001	Min. :0.011	Min. :0.006	Min. : 2.473	Min. : 1.773	Min. : 0.494	Min. : 0.000	Min. : 0.0	Min. :0.0000	Min. : 24198	Min. :1.946e+08	Min. :5.198e+11	Min. :7.913e+06	Min. :0.231	Min. :0.018	Min. :0.000	Min. : 0.000	Min. :6.817e+05	Min. :1.231e+11	Min. :9.400e+10	Min. :3.183e+06	Min. :3.774e+03	Min. :5.528e+09	Min. : 0.064	Min. : 0.001	Min. : 0.001	Min. : 0.000	Min. : 0.000	Min. : 0.005	Min. :0.000	Min. :0.000	Min. :0.000	Min. :0.000	Min. :0.000	Min. :0	Min. : 1023	Min. :0.001	Min. :0.009	Min. :0.010	Min. : 2.869	Min. : 1.644	Min. : 0.496	Min. : 6632	Min. :1.333e+07	Min. :7.807e+09	Min. :1.426e+06	Min. : 0.058	Min. : 0.001	Min. :0.000	Min. : 0.000	Min. :2.208e+05	Min. :1.088e+10	Min. :1.331e+10	Min. :5.746e+05	Min. :3.000e+00	Min. :5.871e+08	Min. : 0.005	Min. : 0.000	Min. : 0.00	Min. : 0.000	Min. : 0.000	Min. : 0.000	Min. :0.000	Min. :0.000	Min. :0.000	Min. :0.000	Min. :0.000	Min. :0.000	Min. : 260.4	Min. :0.001	Min. :0.008	Min. :0.011	Min. : 2.577	Min. : 1.617	Min. : 0.497	Min. : 0.000	Min. : 0.000	Min. :0.00	Min. : 24236	Min. :1.956e+08	Min. :5.224e+11	Min. :4.696e+06	Min. :0.231	Min. :0.018	Min. :0.000	Min. : 0.000	Min. :6.861e+05	Min. :1.254e+11	Min. :9.477e+10	Min. :1.895e+06	Min. :5.507e+03	Min. :5.553e+09	Min. : 0.064	Min. : 0.001	Min. : 0.001	Min. : 0.000	Min. : 0.000	Min. : 0.005	Min. :0.000	Min. :0.000	Min. :0.000	Min. :0.000	Min. :0.000	Min. :0	Min. : 1023	Min. :0.001	Min. :0.008	Min. :0.011	Min. : 2.866	Min. : 2.056	Min. : 0.496	Min. : 6155	Min. :1.161e+07	Min. :6.600e+09	Min. :8.006e+05	Min. : 0.058	Min. :0.001	Min. :0.000	Min. : 0.000	Min. :1.955e+05	Min. :9.552e+09	Min. :8.451e+09	Min. :3.227e+05	Min. :8.210e+02	Min. :4.207e+08	Min. : 0.005	Min. : 0.000	Min. : 0.0	Min. : 0.000	Min. : 0.000	Min. : 0.000	Min. :0.000	Min. :0.000	Min. : 0.000	Min. :0.000	Min. :0.000	Min. :0.000	Min. : 234.8	Min. :0.001	Min. :0.008	Min. :0.013	Min. : 2.538	Min. : 1.980	Min. : 0.496	Min. : 0.00	Min. : 0.00	Min. :0.0000	Min. : 24358	Min. :1.971e+08	Min. :5.177e+11	Min. :7.396e+06	Min. : 0.231	Min. :0.018	Min. :0.000	Min. : 0.000	Min. :6.850e+05	Min. :1.250e+11	Min. :9.408e+10	Min. :2.975e+06	Min. :4.763e+03	Min. :5.566e+09	Min. : 0.064	Min. : 0.001	Min. : 0.001	Min. : 0.000	Min. : 0.000	Min. : 0.005	Min. :0.000	Min. :0.000	Min. :0.000	Min. :0.00	Min. :0.000	Min. :0	Min. : 1023	Min. :0.001	Min. :0.008	Min. :0.012	Min. : 2.853	Min. : 2.037	Min. : 0.496	Min. : 6900	Min. :1.522e+07	Min. :1.075e+10	Min. :2.231e+06	Min. : 0.057	Min. :0.001	Min. :0.000	Min. : 0.000	Min. :2.044e+05	Min. :1.033e+10	Min. :1.034e+10	Min. :8.939e+05	Min. :2.507e+03	Min. :5.106e+08	Min. : 0.004	Min. : 0.000	Min. : 0.00	Min. : 0.000	Min. : 0.000	Min. : 0.000	Min. :0.000	Min. :0.000	Min. :0.000	Min. :0.000	Min. :0.00	Min. :0.000	Min. : 292.1	Min. :0.001	Min. :0.007	Min. :0.014	Min. : 2.523	Min. : 1.955	Min. : 0.496	Min. : 0.000	Min. : 0.000	Min. :0.0000	Min. : 24313	Min. :1.960e+08	Min. :5.149e+11	Min. :5.901e+06	Min. :0.231	Min. :0.018	Min. :0.000	Min. : 0.000	Min. :6.911e+05	Min. :1.255e+11	Min. :9.856e+10	Min. :2.436e+06	Min. :2.860e+03	Min. :5.679e+09	Min. : 0.064	Min. : 0.001	Min. : 0.001	Min. : 0.000	Min. : 0.000	Min. : 0.005	Min. :0.000	Min. :0.000	Min. :0.000	Min. :0.000	Min. :0.000	Min. :0	Min. : 1023	Min. :0.006	Min. :0.007	Min. :0.018	Min. : 2.930	Min. : 1.947	Min. : 1.077	Min. : 7722	Min. :1.917e+07	Min. :1.546e+10	Min. :2.261e+06	Min. :0.057	Min. :0.001	Min. :0.000	Min. : 0.000	Min. :2.348e+05	Min. :1.442e+10	Min. :1.161e+10	Min. :9.168e+05	Min. :6.445e+03	Min. :6.453e+08	Min. : 0.004	Min. : 0.000	Min. : 0.000	Min. : 0.000	Min. : 0.000	Min. : 0.000	Min. :0.000	Min. :0.000	Min. :0.000	Min. :0.000	Min. :0.000	Min. :0	Min. : 291.9	Min. :0.005	Min. :0.007	Min. :0.018	Min. : 2.610	Min. : 1.921	Min. : 1.063	Min. : 0.000	Min. : 0.000	Min. :0.0000	Min. : 24284	Min. :1.955e+08	Min. :5.167e+11	Min. :4.143e+06	Min. :0.231	Min. :0.018	Min. :0.000	Min. : 0.000	Min. :6.883e+05	Min. :1.246e+11	Min. :9.780e+10	Min. :1.767e+06	Min. :5.470e+02	Min. :5.625e+09	Min. : 0.064	Min. : 0.001	Min. : 0.001	Min. : 0.000	Min. : 0.000	Min. : 0.005	Min. :0.000	Min. :0.000	Min. :0.000	Min. :0.000	Min. :0.000	Min. :0	Min. : 1023	Min. :0.005	Min. :0.010	Min. :0.020	Min. : 2.942	Min. : 2.154	Min. : 1.051	Min. : 6023	Min. :1.180e+07	Min. :7.555e+09	Min. :1.376e+06	Min. :0.057	Min. :0.001	Min. :0.000	Min. : 0.000	Min. :1.750e+05	Min. :7.599e+09	Min. :7.022e+09	Min. :5.570e+05	Min. :8.525e+03	Min. :3.736e+08	Min. : 0.004	Min. : 0.000	Min. : 0.000	Min. : 0.000	Min. : 0.000	Min. : 0.000	Min. :0.000	Min. :0.000	Min. :0.000	Min. :0.000	Min. :0.00	Min. :0	Min. : 256.5	Min. :0.004	Min. :0.010	Min. :0.020	Min. : 2.620	Min. : 2.106	Min. : 1.041	Min. : 0.000	Min. : 0.000	Min. :0.0000	Min. : 24584	Min. :1.970e+08	Min. :5.129e+11	Min. :4.052e+06	Min. :0.231	Min. :0.018	Min. :0.000	Min. : 0.000	Min. :7.117e+05	Min. :1.303e+11	Min. :1.047e+11	Min. :1.669e+06	Min. :3.899e+03	Min. :5.953e+09	Min. : 0.064	Min. : 0.001	Min. : 0.001	Min. : 0.000	Min. : 0.000	Min. : 0.005	Min. :0.000	Min. :0.000	Min. :0.000	Min. :0.000	Min. :0.00	Min. :0	Min. : 1023	Min. :0.004	Min. :0.011	Min. :0.025	Min. : 2.949	Min. : 2.144	Min. : 1.060	Min. : 7987	Min. :2.102e+07	Min. :1.388e+10	Min. :1.586e+06	Min. : 0.056	Min. : 0.001	Min. :0.000	Min. : 0.000	Min. :2.621e+05	Min. :1.642e+10	Min. :1.529e+10	Min. :6.442e+05	Min. :1.606e+04	Min. :7.249e+08	Min. : 0.004	Min. : 0.000	Min. : 0.000	Min. : 0.000	Min. : 0.000	Min. : 0.000	Min. :0.000	Min. :0.000	Min. :0.000	Min. :0.000	Min. :0.000	Min. :0.000	Min. : 297.4	Min. :0.004	Min. :0.010	Min. :0.025	Min. : 2.598	Min. : 2.108	Min. : 1.045	Min. : 64.77	Min. : 0.000	Min. :0.00000	Min. :0.0000	Min. :0	Min. : 0.000	Min. :0.0000	Min. :0.0000	DELFWT:14132	PHDELWT:14132	Mode:logical	Min. :0.2	Min. :1.9	Min. :1.4	Min. :2	Min. :0.800	Min. :-2.992e-06	Min. :0.009962	Min. :0.0003424	Min. :-2.3692	Min. : 0.2245	Min. :-3.028e-06	Min. :0.01847	Min. :0.003589	Min. :-3.3935	Min. : 0.2999	Min. :-2.206e-06	Min. :0.009009	Min. :0.0001201	Min. :-26.24490	Min. : 0.07625	Min. :-7.867e-07	Min. :0.005332	Min. :0.000403	Min. :-3.2550	Min. : 0.2407	Min. : 0	Min. : 5472	Min. : 15008	Min. :0.0000	Min. :-0.30666	Min. :0.00930	Min. :-0.2850280	Min. :0.02783	Min. :6.595e-05	Min. :0.03485	Min. :-0.08705	Min. :0.01120	Min. :-0.24222	Min. :0.03827	Min. :-0.003379	Min. :0.05494	Min. :-0.08982	Min. :0.00893	Min. :-0.095296	Min. :0.01336	Min. :-0.11559	Min. :0.01705	Min. :-0.21683	Min. :0.00345	Min. :-0.239831	Min. :0.01529	Min. :-0.005495	Min. :0.02092	Min. :-0.66520	Min. :0.01075	Min. :0.000e+00	Min. :0.000e+00	Min. :-11.2668	Min. :-0.31511	Min. :0.00918	Min. :2.5	Min. :7.1	Min. :0.5
	111l CL 173 A : 1	3abl : 15	ZN : 849	1 : 424	B :2905	1st Qu.: NA	1st Qu.: NA	1st Qu.: NA	1st Qu.: NA	1st Qu.: NA	1st Qu.: NA	1st Qu.: NA	1st Qu.: NA	1st Qu.: NA	1st Qu.: NA	1st Qu.: NA	1st Qu.: 1.00	1st Qu.: 1.00	1st Qu.: 1.00	1st Qu.: 30.0	1st Qu.: 30.00	1st Qu.: 0.000	1st Qu.: 0.00	1st Qu.: 0.000	1st Qu.:0.0000	1st Qu.: 1.00	1st Qu.: 30.0	1st Qu.: 0.00	1st Qu.: 0.000	1st Qu.: 0.000	1st Qu.:0.0000	1st Qu.: 9.469	1st Qu.: 14.44	1st Qu.:1.0000	1st Qu.: 106958	1st Qu.:2.876e+09	1st Qu.:2.121e+13	1st Qu.:4.427e+10	1st Qu.:0.3173	1st Qu.:0.0275	1st Qu.:0.0007	1st Qu.:0.0021	1st Qu.:8.630e+06	1st Qu.:1.181e+13	1st Qu.:2.797e+13	1st Qu.:2.340e+10	1st Qu.:5.138e+09	1st Qu.:4.161e+11	1st Qu.: 0.1478	1st Qu.: 0.0037	1st Qu.: 0.0082	1st Qu.:0.0011	1st Qu.:0.0002	1st Qu.: 0.0202	1st Qu.:0.0007	1st Qu.:0.0000	1st Qu.:0.0000	1st Qu.:0.0000	1st Qu.:0e+00	1st Qu.:0	1st Qu.: 1900	1st Qu.:0.0715	1st Qu.:0.1974	1st Qu.:0.3046	1st Qu.: 5.594	1st Qu.: 3.546	1st Qu.: 2.654	1st Qu.: 62839	1st Qu.:1.048e+09	1st Qu.:4.744e+12	1st Qu.:1.340e+10	1st Qu.:0.3736	1st Qu.:0.0376	1st Qu.:0.0011	1st Qu.: 0.0033	1st Qu.:4.534e+06	1st Qu.:3.276e+12	1st Qu.:7.603e+12	1st Qu.:7.514e+09	1st Qu.:2.054e+09	1st Qu.:1.222e+11	1st Qu.: 0.2201	1st Qu.: 0.0084	1st Qu.: 0.0169	1st Qu.: 0.0020	1st Qu.: 0.0006	1st Qu.: 0.0343	1st Qu.:0.0014	1st Qu.:0.0000	1st Qu.:0.0000	1st Qu.:0.0000	1st Qu.:0.0000	1st Qu.:0.0000	1st Qu.: 1263	1st Qu.:0.0678	1st Qu.:0.1912	1st Qu.:0.2997	1st Qu.: 5.086	1st Qu.: 3.263	1st Qu.: 2.4659	1st Qu.: 8.581	1st Qu.: 12.77	1st Qu.:1.0000	1st Qu.: 99934	1st Qu.:2.480e+09	1st Qu.:1.702e+13	1st Qu.:3.533e+10	1st Qu.:0.3104	1st Qu.:0.0267	1st Qu.:0.0007	1st Qu.:0.0019	1st Qu.:7.759e+06	1st Qu.:9.470e+12	1st Qu.:2.146e+13	1st Qu.:1.857e+10	1st Qu.:3.874e+09	1st Qu.:3.399e+11	1st Qu.: 0.1405	1st Qu.: 0.0034	1st Qu.: 0.0072	1st Qu.:0.0010	1st Qu.:0.0002	1st Qu.: 0.0184	1st Qu.:0.0007	1st Qu.:0.0000	1st Qu.:0.000	1st Qu.:0.0000	1st Qu.:0.000	1st Qu.:0	1st Qu.: 1829	1st Qu.:0.0687	1st Qu.:0.1929	1st Qu.:0.2994	1st Qu.: 5.444	1st Qu.: 3.499	1st Qu.: 2.6289	1st Qu.: 61609	1st Qu.:1.002e+09	1st Qu.:4.474e+12	1st Qu.:1.122e+10	1st Qu.:0.3578	1st Qu.:0.0353	1st Qu.:0.0010	1st Qu.: 0.0027	1st Qu.:4.245e+06	1st Qu.:2.887e+12	1st Qu.:6.328e+12	1st Qu.:6.274e+09	1st Qu.:1.645e+09	1st Qu.:1.122e+11	1st Qu.: 0.1991	1st Qu.: 0.0072	1st Qu.: 0.014	1st Qu.: 0.0015	1st Qu.: 0.0004	1st Qu.: 0.0295	1st Qu.:0.0013	1st Qu.:0.0000	1st Qu.:0.0000	1st Qu.:0.0000	1st Qu.:0.0000	1st Qu.:0.0000	1st Qu.: 1279.76	1st Qu.:0.0653	1st Qu.:0.1865	1st Qu.:0.2938	1st Qu.: 4.912	1st Qu.: 3.223	1st Qu.: 2.4489	1st Qu.: 5.98	1st Qu.: 9.488	1st Qu.:1.0000	1st Qu.: 87509	1st Qu.:1.981e+09	1st Qu.:1.221e+13	1st Qu.:2.353e+10	1st Qu.:0.2974	1st Qu.:0.0255	1st Qu.:0.0006	1st Qu.: 0.0016	1st Qu.:6.300e+06	1st Qu.:6.696e+12	1st Qu.:1.390e+13	1st Qu.:1.204e+10	1st Qu.:2.229e+09	1st Qu.:2.418e+11	1st Qu.: 0.1256	1st Qu.: 0.0030	1st Qu.: 0.0053	1st Qu.: 0.0008	1st Qu.: 0.0001	1st Qu.: 0.0154	1st Qu.:0.0007	1st Qu.:0.0000	1st Qu.:0.0000	1st Qu.:0.0000	1st Qu.:0.0000	1st Qu.:0	1st Qu.: 1721	1st Qu.:0.0625	1st Qu.:0.1858	1st Qu.:0.2851	1st Qu.: 5.152	1st Qu.: 3.424	1st Qu.: 2.6169	1st Qu.: 60814	1st Qu.:9.935e+08	1st Qu.:4.544e+12	1st Qu.:8.991e+09	1st Qu.:0.3269	1st Qu.:0.0304	1st Qu.:0.0008	1st Qu.: 0.0017	1st Qu.:3.855e+06	1st Qu.:2.554e+12	1st Qu.:4.996e+12	1st Qu.:4.610e+09	1st Qu.:1.031e+09	1st Qu.:9.700e+10	1st Qu.: 0.1632	1st Qu.: 0.0050	1st Qu.: 0.009	1st Qu.: 0.0009	1st Qu.: 0.0002	1st Qu.: 0.0213	1st Qu.:0.0011	1st Qu.:0.0000	1st Qu.:0.0000	1st Qu.:0.0000	1st Qu.:0.0000	1st Qu.:0.0000	1st Qu.: 1345.6	1st Qu.:0.0602	1st Qu.:0.1798	1st Qu.:0.2815	1st Qu.: 4.652	1st Qu.: 3.168	1st Qu.: 2.4482	1st Qu.: 0.0	1st Qu.: 0.00	1st Qu.:0.0000	1st Qu.: 80446	1st Qu.:1.677e+09	1st Qu.:9.703e+12	1st Qu.:1.576e+10	1st Qu.:0.286	1st Qu.:0.024	1st Qu.:0.001	1st Qu.: 0.001	1st Qu.:5.225e+06	1st Qu.:4.915e+12	1st Qu.:9.243e+12	1st Qu.:7.840e+09	1st Qu.:1.139e+09	1st Qu.:1.751e+11	1st Qu.: 0.113	1st Qu.: 0.003	1st Qu.: 0.004	1st Qu.: 0.001	1st Qu.: 0.000	1st Qu.: 0.013	1st Qu.:0.001	1st Qu.:0.000	1st Qu.:0.00	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0	1st Qu.: 1641	1st Qu.:0.058	1st Qu.:0.178	1st Qu.:0.276	1st Qu.: 4.844	1st Qu.: 3.364	1st Qu.: 2.599	1st Qu.: 60306	1st Qu.:9.973e+08	1st Qu.:4.631e+12	1st Qu.:6.872e+09	1st Qu.: 0.297	1st Qu.: 0.025	1st Qu.:0.001	1st Qu.: 0.001	1st Qu.:3.494e+06	1st Qu.:2.266e+12	1st Qu.:3.813e+12	1st Qu.:3.509e+09	1st Qu.:5.793e+08	1st Qu.:8.407e+10	1st Qu.: 0.131	1st Qu.: 0.003	1st Qu.: 0.01	1st Qu.: 0.001	1st Qu.: 0.000	1st Qu.: 0.016	1st Qu.:0.001	1st Qu.:0.000	1st Qu.: 0.000	1st Qu.:0.00	1st Qu.:0.000	1st Qu.:0.000	1st Qu.: 1421	1st Qu.:0.057	1st Qu.:0.173	1st Qu.:0.273	1st Qu.: 4.382	1st Qu.: 3.117	1st Qu.: 2.435	1st Qu.: 0.000	1st Qu.: 0.0	1st Qu.:0.0000	1st Qu.: 71079	1st Qu.:1.326e+09	1st Qu.:6.878e+12	1st Qu.:1.046e+10	1st Qu.:0.279	1st Qu.:0.024	1st Qu.:0.001	1st Qu.: 0.001	1st Qu.:4.357e+06	1st Qu.:3.416e+12	1st Qu.:6.267e+12	1st Qu.:4.954e+09	1st Qu.:6.858e+08	1st Qu.:1.308e+11	1st Qu.: 0.105	1st Qu.: 0.002	1st Qu.: 0.003	1st Qu.: 0.000	1st Qu.: 0.000	1st Qu.: 0.011	1st Qu.:0.001	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0	1st Qu.: 1532	1st Qu.:0.055	1st Qu.:0.174	1st Qu.:0.267	1st Qu.: 4.606	1st Qu.: 3.294	1st Qu.: 2.562	1st Qu.: 59308	1st Qu.:9.687e+08	1st Qu.:4.582e+12	1st Qu.:5.444e+09	1st Qu.: 0.275	1st Qu.: 0.022	1st Qu.:0.001	1st Qu.: 0.001	1st Qu.:3.176e+06	1st Qu.:1.856e+12	1st Qu.:2.981e+12	1st Qu.:2.664e+09	1st Qu.:3.907e+08	1st Qu.:7.490e+10	1st Qu.: 0.111	1st Qu.: 0.002	1st Qu.: 0.00	1st Qu.: 0.000	1st Qu.: 0.000	1st Qu.: 0.012	1st Qu.:0.001	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0.000	1st Qu.: 1439.6	1st Qu.:0.054	1st Qu.:0.168	1st Qu.:0.265	1st Qu.: 4.201	1st Qu.: 3.066	1st Qu.: 2.413	1st Qu.: 0.000	1st Qu.: 0.000	1st Qu.:0.00	1st Qu.: 63638	1st Qu.:1.119e+09	1st Qu.:5.454e+12	1st Qu.:7.214e+09	1st Qu.:0.273	1st Qu.:0.023	1st Qu.:0.001	1st Qu.: 0.001	1st Qu.:3.620e+06	1st Qu.:2.433e+12	1st Qu.:4.051e+12	1st Qu.:3.319e+09	1st Qu.:3.608e+08	1st Qu.:9.492e+10	1st Qu.: 0.099	1st Qu.: 0.002	1st Qu.: 0.003	1st Qu.: 0.000	1st Qu.: 0.000	1st Qu.: 0.010	1st Qu.:0.001	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0	1st Qu.: 1461	1st Qu.:0.054	1st Qu.:0.169	1st Qu.:0.273	1st Qu.: 4.408	1st Qu.: 3.226	1st Qu.: 2.530	1st Qu.: 58067	1st Qu.:9.551e+08	1st Qu.:4.472e+12	1st Qu.:4.431e+09	1st Qu.: 0.253	1st Qu.:0.019	1st Qu.:0.000	1st Qu.: 0.001	1st Qu.:2.854e+06	1st Qu.:1.644e+12	1st Qu.:2.343e+12	1st Qu.:2.080e+09	1st Qu.:2.396e+08	1st Qu.:6.389e+10	1st Qu.: 0.094	1st Qu.: 0.002	1st Qu.: 0.0	1st Qu.: 0.000	1st Qu.: 0.000	1st Qu.: 0.009	1st Qu.:0.001	1st Qu.:0.000	1st Qu.: 0.000	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0.000	1st Qu.: 1473.5	1st Qu.:0.053	1st Qu.:0.163	1st Qu.:0.271	1st Qu.: 4.032	1st Qu.: 3.000	1st Qu.: 2.393	1st Qu.: 0.00	1st Qu.: 0.00	1st Qu.:0.0000	1st Qu.: 57285	1st Qu.:9.262e+08	1st Qu.:4.274e+12	1st Qu.:4.645e+09	1st Qu.: 0.266	1st Qu.:0.022	1st Qu.:0.001	1st Qu.: 0.001	1st Qu.:2.981e+06	1st Qu.:1.764e+12	1st Qu.:2.516e+12	1st Qu.:2.130e+09	1st Qu.:2.076e+08	1st Qu.:6.809e+10	1st Qu.: 0.093	1st Qu.: 0.002	1st Qu.: 0.002	1st Qu.: 0.000	1st Qu.: 0.000	1st Qu.: 0.009	1st Qu.:0.001	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0.00	1st Qu.:0.000	1st Qu.:0	1st Qu.: 1401	1st Qu.:0.056	1st Qu.:0.166	1st Qu.:0.287	1st Qu.: 4.190	1st Qu.: 3.164	1st Qu.: 2.517	1st Qu.: 56014	1st Qu.:9.081e+08	1st Qu.:4.089e+12	1st Qu.:3.213e+09	1st Qu.: 0.234	1st Qu.:0.017	1st Qu.:0.000	1st Qu.: 0.000	1st Qu.:2.507e+06	1st Qu.:1.371e+12	1st Qu.:1.719e+12	1st Qu.:1.521e+09	1st Qu.:1.409e+08	1st Qu.:5.238e+10	1st Qu.: 0.080	1st Qu.: 0.001	1st Qu.: 0.00	1st Qu.: 0.000	1st Qu.: 0.000	1st Qu.: 0.007	1st Qu.:0.001	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0.00	1st Qu.:0.000	1st Qu.: 1485.3	1st Qu.:0.056	1st Qu.:0.162	1st Qu.:0.288	1st Qu.: 3.841	1st Qu.: 2.954	1st Qu.: 2.384	1st Qu.: 0.000	1st Qu.: 0.000	1st Qu.:0.0000	1st Qu.: 51008	1st Qu.:7.495e+08	1st Qu.:3.256e+12	1st Qu.:2.976e+09	1st Qu.:0.259	1st Qu.:0.021	1st Qu.:0.001	1st Qu.: 0.000	1st Qu.:2.419e+06	1st Qu.:1.272e+12	1st Qu.:1.559e+12	1st Qu.:1.351e+09	1st Qu.:1.110e+08	1st Qu.:4.633e+10	1st Qu.: 0.087	1st Qu.: 0.002	1st Qu.: 0.002	1st Qu.: 0.000	1st Qu.: 0.000	1st Qu.: 0.008	1st Qu.:0.001	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0	1st Qu.: 1348	1st Qu.:0.061	1st Qu.:0.171	1st Qu.:0.321	1st Qu.: 3.990	1st Qu.: 3.089	1st Qu.: 2.522	1st Qu.: 53459	1st Qu.:8.456e+08	1st Qu.:3.990e+12	1st Qu.:2.255e+09	1st Qu.:0.220	1st Qu.:0.015	1st Qu.:0.000	1st Qu.: 0.000	1st Qu.:2.225e+06	1st Qu.:1.118e+12	1st Qu.:1.272e+12	1st Qu.:1.056e+09	1st Qu.:8.450e+07	1st Qu.:4.341e+10	1st Qu.: 0.069	1st Qu.: 0.001	1st Qu.: 0.001	1st Qu.: 0.000	1st Qu.: 0.000	1st Qu.: 0.006	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0	1st Qu.: 1511.6	1st Qu.:0.061	1st Qu.:0.165	1st Qu.:0.318	1st Qu.: 3.670	1st Qu.: 2.887	1st Qu.: 2.384	1st Qu.: 0.000	1st Qu.: 0.000	1st Qu.:0.0000	1st Qu.: 46145	1st Qu.:6.309e+08	1st Qu.:2.522e+12	1st Qu.:1.987e+09	1st Qu.:0.255	1st Qu.:0.021	1st Qu.:0.001	1st Qu.: 0.000	1st Qu.:2.023e+06	1st Qu.:9.101e+11	1st Qu.:1.081e+12	1st Qu.:8.812e+08	1st Qu.:6.662e+07	1st Qu.:3.486e+10	1st Qu.: 0.082	1st Qu.: 0.002	1st Qu.: 0.002	1st Qu.: 0.000	1st Qu.: 0.000	1st Qu.: 0.007	1st Qu.:0.001	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0	1st Qu.: 1298	1st Qu.:0.071	1st Qu.:0.170	1st Qu.:0.371	1st Qu.: 3.840	1st Qu.: 3.024	1st Qu.: 2.518	1st Qu.: 52102	1st Qu.:8.038e+08	1st Qu.:3.678e+12	1st Qu.:1.671e+09	1st Qu.:0.211	1st Qu.:0.013	1st Qu.:0.000	1st Qu.: 0.000	1st Qu.:2.021e+06	1st Qu.:9.610e+11	1st Qu.:1.025e+12	1st Qu.:7.524e+08	1st Qu.:5.455e+07	1st Qu.:3.837e+10	1st Qu.: 0.062	1st Qu.: 0.001	1st Qu.: 0.001	1st Qu.: 0.000	1st Qu.: 0.000	1st Qu.: 0.005	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0.00	1st Qu.:0	1st Qu.: 1531.5	1st Qu.:0.071	1st Qu.:0.162	1st Qu.:0.369	1st Qu.: 3.547	1st Qu.: 2.827	1st Qu.: 2.378	1st Qu.: 0.000	1st Qu.: 0.000	1st Qu.:0.0000	1st Qu.: 43744	1st Qu.:5.732e+08	1st Qu.:2.284e+12	1st Qu.:1.446e+09	1st Qu.:0.251	1st Qu.:0.020	1st Qu.:0.001	1st Qu.: 0.000	1st Qu.:1.885e+06	1st Qu.:8.044e+11	1st Qu.:8.956e+11	1st Qu.:6.424e+08	1st Qu.:4.822e+07	1st Qu.:3.039e+10	1st Qu.: 0.080	1st Qu.: 0.002	1st Qu.: 0.002	1st Qu.: 0.000	1st Qu.: 0.000	1st Qu.: 0.007	1st Qu.:0.001	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0.00	1st Qu.:0	1st Qu.: 1283	1st Qu.:0.083	1st Qu.:0.164	1st Qu.:0.415	1st Qu.: 3.771	1st Qu.: 2.979	1st Qu.: 2.513	1st Qu.: 50807	1st Qu.:7.782e+08	1st Qu.:3.589e+12	1st Qu.:1.327e+09	1st Qu.: 0.205	1st Qu.: 0.013	1st Qu.:0.000	1st Qu.: 0.000	1st Qu.:1.936e+06	1st Qu.:8.855e+11	1st Qu.:9.161e+11	1st Qu.:6.017e+08	1st Qu.:4.189e+07	1st Qu.:3.637e+10	1st Qu.: 0.058	1st Qu.: 0.001	1st Qu.: 0.001	1st Qu.: 0.000	1st Qu.: 0.000	1st Qu.: 0.004	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0.000	1st Qu.:0.000	1st Qu.: 1565.5	1st Qu.:0.082	1st Qu.:0.156	1st Qu.:0.416	1st Qu.: 3.502	1st Qu.: 2.793	1st Qu.: 2.379	1st Qu.: 222.94	1st Qu.: 9.469	1st Qu.:0.02630	1st Qu.:0.1127	1st Qu.:0	1st Qu.: 0.819	1st Qu.:0.1811	1st Qu.:1.0000	NA	NA	NA’s:14132	1st Qu.:0.2	1st Qu.:1.9	1st Qu.:1.4	1st Qu.:2	1st Qu.:1.800	1st Qu.:-7.560e-11	1st Qu.:0.209427	1st Qu.:0.1013822	1st Qu.:-1.1336	1st Qu.: 1.7394	1st Qu.:-6.600e-11	1st Qu.:0.20189	1st Qu.:0.103773	1st Qu.:-1.0312	1st Qu.: 1.6707	1st Qu.:-1.104e-10	1st Qu.:0.099798	1st Qu.:0.0220144	1st Qu.: -0.89931	1st Qu.: 1.29550	1st Qu.:-3.450e-11	1st Qu.:0.188691	1st Qu.:0.094683	1st Qu.:-0.9041	1st Qu.: 1.5904	1st Qu.: 303069	1st Qu.: 316088	1st Qu.: 522748	1st Qu.:0.2312	1st Qu.:-0.00873	1st Qu.:0.08642	1st Qu.:-0.1686900	1st Qu.:0.16439	1st Qu.:7.875e-02	1st Qu.:0.28159	1st Qu.:-0.00762	1st Qu.:0.05653	1st Qu.:-0.15855	1st Qu.:0.14448	1st Qu.: 0.073746	1st Qu.:0.28148	1st Qu.:-0.00317	1st Qu.:0.08416	1st Qu.:-0.028594	1st Qu.:0.10356	1st Qu.: 0.00600	1st Qu.:0.11254	1st Qu.:-0.00675	1st Qu.:0.03232	1st Qu.:-0.143829	1st Qu.:0.12733	1st Qu.: 0.066065	1st Qu.:0.26695	1st Qu.:-0.33681	1st Qu.:0.12728	1st Qu.:0.000e+00	1st Qu.:0.000e+00	1st Qu.: 0.3844	1st Qu.:-0.00788	1st Qu.:0.08475	1st Qu.:2.5	1st Qu.:7.1	1st Qu.:0.5
	115l BME 901 A: 1	3abk : 14	GOL : 778	501 : 417	C : 795	Median : NA	Median : NA	Median : NA	Median : NA	Median : NA	Median : NA	Median : NA	Median : NA	Median : NA	Median : NA	Median : NA	Median : 7.00	Median : 7.00	Median : 7.00	Median : 55.0	Median : 52.00	Median : 3.000	Median : 0.00	Median : 3.000	Median :0.0000	Median : 7.00	Median : 56.0	Median : 3.00	Median : 0.000	Median : 3.000	Median :0.0000	Median : 18.614	Median : 27.30	Median :1.0000	Median : 392324	Median :3.374e+10	Median :7.154e+14	Median :1.747e+12	Median :0.4826	Median :0.0508	Median :0.0012	Median :0.0129	Median :6.683e+07	Median :5.855e+14	Median :2.106e+15	Median :1.004e+12	Median :3.677e+11	Median :1.259e+13	Median : 0.3602	Median : 0.0167	Median : 0.0633	Median :0.0074	Median :0.0026	Median : 0.0873	Median :0.0014	Median :0.0000	Median :0.0000	Median :0.0000	Median :0e+00	Median :0	Median : 3580	Median :0.1328	Median :0.3432	Median :0.5061	Median : 8.634	Median : 4.749	Median : 3.275	Median : 201961	Median :8.933e+09	Median :1.036e+14	Median :4.992e+11	Median :0.6608	Median :0.0950	Median :0.0030	Median : 0.0331	Median :3.292e+07	Median :1.356e+14	Median :5.501e+14	Median :3.324e+11	Median :1.688e+11	Median :3.315e+12	Median : 0.7094	Median : 0.0617	Median : 0.2383	Median : 0.0212	Median : 0.0092	Median : 0.2346	Median :0.0035	Median :0.0000	Median :0.0000	Median :0.0000	Median :0.0000	Median :0.0000	Median : 2421	Median :0.1277	Median :0.3387	Median :0.5086	Median : 8.278	Median : 4.407	Median : 3.0316	Median : 17.561	Median : 24.51	Median :1.0000	Median : 371987	Median :3.003e+10	Median :5.893e+14	Median :1.510e+12	Median :0.4898	Median :0.0516	Median :0.0012	Median :0.0133	Median :6.310e+07	Median :4.979e+14	Median :1.913e+15	Median :8.845e+11	Median :3.127e+11	Median :1.150e+13	Median : 0.3698	Median : 0.0172	Median : 0.0669	Median :0.0077	Median :0.0028	Median : 0.0905	Median :0.0014	Median :0.0000	Median :0.000	Median :0.0000	Median :0.000	Median :0	Median : 3438	Median :0.1310	Median :0.3406	Median :0.5083	Median : 8.615	Median : 4.687	Median : 3.2292	Median : 202072	Median :8.784e+09	Median :9.831e+13	Median :4.894e+11	Median :0.6567	Median :0.0935	Median :0.0029	Median : 0.0320	Median :3.248e+07	Median :1.308e+14	Median :5.311e+14	Median :3.206e+11	Median :1.575e+11	Median :3.324e+12	Median : 0.6992	Median : 0.0591	Median : 0.234	Median : 0.0207	Median : 0.0089	Median : 0.2290	Median :0.0035	Median :0.0000	Median :0.0000	Median :0.0000	Median :0.0000	Median :0.0000	Median : 2417.00	Median :0.1259	Median :0.3374	Median :0.5097	Median : 8.306	Median : 4.348	Median : 2.9975	Median : 14.92	Median : 18.472	Median :1.0000	Median : 353222	Median :2.632e+10	Median :4.586e+14	Median :1.271e+12	Median :0.5084	Median :0.0541	Median :0.0013	Median : 0.0147	Median :5.957e+07	Median :4.354e+14	Median :1.765e+15	Median :7.309e+11	Median :2.667e+11	Median :1.056e+13	Median : 0.3991	Median : 0.0192	Median : 0.0794	Median : 0.0086	Median : 0.0031	Median : 0.1038	Median :0.0016	Median :0.0000	Median :0.0000	Median :0.0000	Median :0.0000	Median :0	Median : 3199	Median :0.1240	Median :0.3398	Median :0.5108	Median : 8.686	Median : 4.593	Median : 3.1510	Median : 217227	Median :9.850e+09	Median :1.054e+14	Median :5.136e+11	Median :0.6472	Median :0.0889	Median :0.0027	Median : 0.0303	Median :3.677e+07	Median :1.554e+14	Median :7.150e+14	Median :3.379e+11	Median :1.608e+11	Median :4.074e+12	Median : 0.6657	Median : 0.0525	Median : 0.219	Median : 0.0196	Median : 0.0080	Median : 0.2166	Median :0.0034	Median :0.0000	Median :0.0000	Median :0.0000	Median :0.0000	Median :0.0000	Median : 2471.8	Median :0.1191	Median :0.3397	Median :0.5135	Median : 8.412	Median : 4.310	Median : 2.9366	Median : 12.2	Median : 13.93	Median :1.0000	Median : 332182	Median :2.274e+10	Median :3.814e+14	Median :1.155e+12	Median :0.523	Median :0.056	Median :0.001	Median : 0.016	Median :5.614e+07	Median :3.576e+14	Median :1.544e+15	Median :6.599e+11	Median :2.269e+11	Median :9.433e+12	Median : 0.420	Median : 0.021	Median : 0.089	Median : 0.009	Median : 0.003	Median : 0.114	Median :0.002	Median :0.000	Median :0.00	Median :0.000	Median :0.000	Median :0	Median : 2992	Median :0.121	Median :0.346	Median :0.518	Median : 8.678	Median : 4.500	Median : 3.088	Median : 228546	Median :1.021e+10	Median :1.089e+14	Median :5.777e+11	Median : 0.628	Median : 0.084	Median :0.002	Median : 0.028	Median :3.763e+07	Median :1.593e+14	Median :7.299e+14	Median :3.627e+11	Median :1.751e+11	Median :4.446e+12	Median : 0.630	Median : 0.047	Median : 0.20	Median : 0.018	Median : 0.008	Median : 0.200	Median :0.003	Median :0.000	Median : 0.000	Median :0.00	Median :0.000	Median :0.000	Median : 2517	Median :0.117	Median :0.347	Median :0.521	Median : 8.473	Median : 4.238	Median : 2.885	Median : 9.215	Median : 10.6	Median :1.0000	Median : 280935	Median :1.629e+10	Median :2.224e+14	Median :7.137e+11	Median :0.525	Median :0.056	Median :0.001	Median : 0.015	Median :4.370e+07	Median :2.246e+14	Median :9.829e+14	Median :4.405e+11	Median :1.563e+11	Median :6.147e+12	Median : 0.429	Median : 0.021	Median : 0.091	Median : 0.009	Median : 0.003	Median : 0.116	Median :0.002	Median :0.000	Median :0.000	Median :0.000	Median :0.000	Median :0	Median : 2709	Median :0.119	Median :0.350	Median :0.527	Median : 8.460	Median : 4.360	Median : 3.012	Median : 208895	Median :8.384e+09	Median :8.358e+13	Median :4.365e+11	Median : 0.594	Median : 0.076	Median :0.002	Median : 0.024	Median :3.302e+07	Median :1.173e+14	Median :6.077e+14	Median :2.828e+11	Median :1.362e+11	Median :3.757e+12	Median : 0.569	Median : 0.040	Median : 0.16	Median : 0.015	Median : 0.006	Median : 0.172	Median :0.003	Median :0.000	Median :0.000	Median :0.000	Median :0.000	Median :0.000	Median : 2479.6	Median :0.114	Median :0.348	Median :0.533	Median : 8.245	Median : 4.104	Median : 2.825	Median : 3.495	Median : 8.224	Median :1.00	Median : 235095	Median :1.149e+10	Median :1.206e+14	Median :4.385e+11	Median :0.516	Median :0.053	Median :0.001	Median : 0.014	Median :3.362e+07	Median :1.342e+14	Median :5.714e+14	Median :2.675e+11	Median :9.867e+10	Median :4.056e+12	Median : 0.406	Median : 0.019	Median : 0.081	Median : 0.008	Median : 0.003	Median : 0.108	Median :0.002	Median :0.000	Median :0.000	Median :0.000	Median :0.000	Median :0	Median : 2462	Median :0.121	Median :0.350	Median :0.548	Median : 8.105	Median : 4.177	Median : 2.942	Median : 191922	Median :7.194e+09	Median :6.487e+13	Median :3.234e+11	Median : 0.545	Median :0.066	Median :0.002	Median : 0.017	Median :2.862e+07	Median :8.374e+13	Median :4.300e+14	Median :2.165e+11	Median :1.037e+11	Median :3.022e+12	Median : 0.481	Median : 0.030	Median : 0.1	Median : 0.011	Median : 0.005	Median : 0.129	Median :0.003	Median :0.000	Median : 0.000	Median :0.000	Median :0.000	Median :0.000	Median : 2418.0	Median :0.117	Median :0.351	Median :0.556	Median : 7.926	Median : 3.941	Median : 2.760	Median : 0.00	Median : 0.00	Median :0.0000	Median : 185677	Median :7.099e+09	Median :6.726e+13	Median :2.095e+11	Median : 0.485	Median :0.048	Median :0.001	Median : 0.010	Median :2.342e+07	Median :6.457e+13	Median :2.739e+14	Median :1.217e+11	Median :4.539e+10	Median :2.319e+12	Median : 0.353	Median : 0.014	Median : 0.061	Median : 0.006	Median : 0.002	Median : 0.090	Median :0.002	Median :0.000	Median :0.000	Median :0.00	Median :0.000	Median :0	Median : 2232	Median :0.129	Median :0.370	Median :0.585	Median : 7.658	Median : 3.950	Median : 2.899	Median : 165427	Median :5.518e+09	Median :4.585e+13	Median :1.829e+11	Median : 0.475	Median :0.051	Median :0.001	Median : 0.009	Median :2.158e+07	Median :4.817e+13	Median :2.463e+14	Median :1.245e+11	Median :5.890e+10	Median :1.962e+12	Median : 0.357	Median : 0.017	Median : 0.06	Median : 0.006	Median : 0.003	Median : 0.085	Median :0.002	Median :0.000	Median :0.000	Median :0.000	Median :0.00	Median :0.000	Median : 2389.9	Median :0.126	Median :0.372	Median :0.594	Median : 7.482	Median : 3.700	Median : 2.717	Median : 0.000	Median : 0.000	Median :0.0000	Median : 150813	Median :4.832e+09	Median :3.949e+13	Median :8.898e+10	Median :0.441	Median :0.042	Median :0.001	Median : 0.006	Median :1.700e+07	Median :3.448e+13	Median :1.447e+14	Median :5.238e+10	Median :2.114e+10	Median :1.353e+12	Median : 0.298	Median : 0.010	Median : 0.043	Median : 0.003	Median : 0.001	Median : 0.067	Median :0.002	Median :0.000	Median :0.000	Median :0.000	Median :0.000	Median :0	Median : 2070	Median :0.145	Median :0.394	Median :0.637	Median : 7.263	Median : 3.706	Median : 2.857	Median : 144521	Median :4.340e+09	Median :3.527e+13	Median :9.948e+10	Median :0.414	Median :0.040	Median :0.001	Median : 0.004	Median :1.634e+07	Median :2.840e+13	Median :1.462e+14	Median :6.826e+10	Median :3.280e+10	Median :1.242e+12	Median : 0.255	Median : 0.009	Median : 0.029	Median : 0.003	Median : 0.001	Median : 0.052	Median :0.002	Median :0.000	Median :0.000	Median :0.000	Median :0.000	Median :0	Median : 2363.1	Median :0.140	Median :0.400	Median :0.654	Median : 7.036	Median : 3.471	Median : 2.681	Median : 0.000	Median : 0.000	Median :0.0000	Median : 127679	Median :3.534e+09	Median :2.584e+13	Median :4.158e+10	Median :0.407	Median :0.036	Median :0.001	Median : 0.003	Median :1.275e+07	Median :1.843e+13	Median :8.248e+13	Median :2.365e+10	Median :9.899e+09	Median :8.806e+11	Median : 0.254	Median : 0.008	Median : 0.031	Median : 0.002	Median : 0.001	Median : 0.052	Median :0.001	Median :0.000	Median :0.000	Median :0.000	Median :0.000	Median :0	Median : 1916	Median :0.158	Median :0.414	Median :0.684	Median : 6.899	Median : 3.489	Median : 2.817	Median : 127405	Median :3.506e+09	Median :2.632e+13	Median :5.500e+10	Median :0.371	Median :0.030	Median :0.001	Median : 0.002	Median :1.319e+07	Median :1.688e+13	Median :9.255e+13	Median :3.616e+10	Median :1.775e+10	Median :8.919e+11	Median : 0.198	Median : 0.005	Median : 0.017	Median : 0.001	Median : 0.001	Median : 0.037	Median :0.001	Median :0.000	Median :0.000	Median :0.000	Median :0.00	Median :0	Median : 2328.1	Median :0.156	Median :0.421	Median :0.700	Median : 6.616	Median : 3.275	Median : 2.650	Median : 0.000	Median : 0.000	Median :0.0000	Median : 116713	Median :2.822e+09	Median :1.990e+13	Median :2.428e+10	Median :0.392	Median :0.034	Median :0.001	Median : 0.002	Median :1.118e+07	Median :1.360e+13	Median :6.380e+13	Median :1.480e+10	Median :5.684e+09	Median :7.004e+11	Median : 0.234	Median : 0.006	Median : 0.027	Median : 0.001	Median : 0.000	Median : 0.046	Median :0.001	Median :0.000	Median :0.000	Median :0.000	Median :0.00	Median :0	Median : 1847	Median :0.167	Median :0.413	Median :0.722	Median : 6.706	Median : 3.360	Median : 2.773	Median : 124612	Median :3.209e+09	Median :2.297e+13	Median :3.406e+10	Median : 0.347	Median : 0.026	Median :0.001	Median : 0.001	Median :1.204e+07	Median :1.361e+13	Median :7.942e+13	Median :2.196e+10	Median :1.126e+10	Median :7.923e+11	Median : 0.175	Median : 0.004	Median : 0.014	Median : 0.001	Median : 0.000	Median : 0.030	Median :0.001	Median :0.000	Median :0.000	Median :0.000	Median :0.000	Median :0.000	Median : 2294.0	Median :0.166	Median :0.422	Median :0.734	Median : 6.407	Median : 3.166	Median : 2.618	Median : 414.56	Median : 18.614	Median :0.03704	Median :0.1532	Median :0	Median : 1.281	Median :0.2529	Median :1.0000	NA	NA	NA	Median :0.2	Median :1.9	Median :1.4	Median :2	Median :2.040	Median : 1.046e-10	Median :0.273372	Median :0.1720145	Median :-0.9347	Median : 2.5344	Median : 1.430e-10	Median :0.26292	Median :0.177083	Median :-0.8379	Median : 2.4168	Median : 6.200e-12	Median :0.130447	Median :0.0413683	Median : -0.71010	Median : 1.93808	Median : 1.402e-10	Median :0.245466	Median :0.163666	Median :-0.7254	Median : 2.3201	Median : 717445	Median : 514708	Median : 885746	Median :0.3320	Median :-0.00457	Median :0.11254	Median :-0.1453470	Median :0.19674	Median :8.654e-02	Median :0.35584	Median :-0.00383	Median :0.07331	Median :-0.13823	Median :0.17009	Median : 0.082145	Median :0.35579	Median :-0.00132	Median :0.10912	Median :-0.018357	Median :0.13054	Median : 0.01149	Median :0.14498	Median :-0.00305	Median :0.04169	Median :-0.126363	Median :0.15054	Median : 0.074527	Median :0.33948	Median :-0.30237	Median :0.14707	Median :0.000e+00	Median :0.000e+00	Median : 0.4401	Median :-0.00367	Median :0.11066	Median :2.5	Median :7.1	Median :0.5
	115l CL 173 A : 1	2afh : 11	CA : 661	401 : 395	D : 552	Mean :NaN	Mean :NaN	Mean :NaN	Mean :NaN	Mean :NaN	Mean :NaN	Mean :NaN	Mean :NaN	Mean :NaN	Mean :NaN	Mean :NaN	Mean : 13.59	Mean : 13.43	Mean : 13.05	Mean :101.5	Mean : 98.33	Mean : 7.332	Mean : 1.35	Mean : 3.817	Mean :0.2208	Mean : 13.74	Mean :103.8	Mean : 7.47	Mean : 1.357	Mean : 3.979	Mean :0.2213	Mean : 31.535	Mean : 51.45	Mean :0.9725	Mean : 3091661	Mean :1.622e+13	Mean :1.938e+20	Mean :2.123e+16	Mean :0.5752	Mean :0.0782	Mean :0.0026	Mean :0.0651	Mean :4.023e+09	Mean :1.143e+20	Mean :6.415e+20	Mean :1.253e+16	Mean :6.720e+15	Mean :1.592e+17	Mean : 0.6958	Mean : 0.1159	Mean : 1.0010	Mean :0.0416	Mean :0.0260	Mean : 0.4266	Mean :0.0022	Mean :0.0000	Mean :0.0000	Mean :0.0000	Mean :0e+00	Mean :0	Mean : 6622	Mean :0.2174	Mean :0.4061	Mean :0.5133	Mean :10.851	Mean : 5.807	Mean : 3.682	Mean : 1670086	Mean :3.188e+12	Mean :4.092e+18	Mean :3.482e+15	Mean :0.7665	Mean :0.1466	Mean :0.0071	Mean : 0.2521	Mean :2.036e+09	Mean :1.025e+19	Mean :7.811e+19	Mean :2.193e+15	Mean :1.334e+15	Mean :2.463e+16	Mean : 1.4490	Mean : 0.6412	Mean : 6.6229	Mean : 0.1849	Mean : 0.1401	Mean : 1.4628	Mean :0.0078	Mean :0.0000	Mean :0.0004	Mean :0.0001	Mean :0.0001	Mean :0.0000	Mean : 4059	Mean :0.2206	Mean :0.4058	Mean :0.5151	Mean :10.400	Mean : 5.487	Mean : 3.4481	Mean : 29.751	Mean : 46.67	Mean :0.9522	Mean : 2894498	Mean :1.384e+13	Mean :1.558e+20	Mean :1.829e+16	Mean :0.5905	Mean :0.0823	Mean :0.0028	Mean :0.0756	Mean :3.739e+09	Mean :9.661e+19	Mean :5.336e+20	Mean :1.094e+16	Mean :6.034e+15	Mean :1.347e+17	Mean : 0.7492	Mean : 0.1351	Mean : 1.2288	Mean :0.0491	Mean :0.0315	Mean : 0.4913	Mean :0.0024	Mean :0.0000	Mean :0.000	Mean :0.0000	Mean :0.000	Mean :0	Mean : 6285	Mean :0.2203	Mean :0.4073	Mean :0.5139	Mean :10.825	Mean : 5.750	Mean : 3.6288	Mean : 1627744	Mean :2.936e+12	Mean :3.562e+18	Mean :3.237e+15	Mean :0.7698	Mean :0.1473	Mean :0.0071	Mean : 0.2734	Mean :1.973e+09	Mean :9.264e+18	Mean :7.123e+19	Mean :2.063e+15	Mean :1.281e+15	Mean :2.274e+16	Mean : 1.4893	Mean : 0.6688	Mean : 7.645	Mean : 0.2013	Mean : 0.1533	Mean : 1.5884	Mean :0.0079	Mean :0.0000	Mean :0.0004	Mean :0.0001	Mean :0.0001	Mean :0.0000	Mean : 4006.64	Mean :0.2236	Mean :0.4073	Mean :0.5159	Mean :10.396	Mean : 5.447	Mean : 3.4090	Mean : 25.29	Mean : 36.283	Mean :0.8789	Mean : 2498087	Mean :9.441e+12	Mean :9.121e+19	Mean :1.266e+16	Mean :0.6297	Mean :0.0941	Mean :0.0033	Mean : 0.1094	Mean :3.159e+09	Mean :6.363e+19	Mean :3.286e+20	Mean :7.812e+15	Mean :4.583e+15	Mean :8.826e+16	Mean : 0.8958	Mean : 0.2128	Mean : 2.0358	Mean : 0.0736	Mean : 0.0498	Mean : 0.6871	Mean :0.0030	Mean :0.0000	Mean :0.0000	Mean :0.0000	Mean :0.0000	Mean :0	Mean : 5623	Mean :0.2274	Mean :0.4111	Mean :0.5145	Mean :10.830	Mean : 5.664	Mean : 3.5406	Mean : 1535388	Mean :2.373e+12	Mean :2.490e+18	Mean :2.667e+15	Mean :0.7767	Mean :0.1502	Mean :0.0071	Mean : 0.3481	Mean :1.830e+09	Mean :7.042e+18	Mean :5.556e+19	Mean :1.749e+15	Mean :1.137e+15	Mean :1.846e+16	Mean : 1.5867	Mean : 0.8918	Mean : 10.868	Mean : 0.2686	Mean : 0.2157	Mean : 1.8945	Mean :0.0081	Mean :0.0000	Mean :0.0005	Mean :0.0001	Mean :0.0001	Mean :0.0000	Mean : 3919.5	Mean :0.2311	Mean :0.4116	Mean :0.5172	Mean :10.432	Mean : 5.387	Mean : 3.3448	Mean : 21.1	Mean : 28.00	Mean :0.7844	Mean : 2143093	Mean :6.474e+12	Mean :5.200e+19	Mean :9.143e+15	Mean :0.664	Mean :0.105	Mean :0.004	Mean : 0.147	Mean :2.643e+09	Mean :4.120e+19	Mean :2.049e+20	Mean :5.802e+15	Mean :3.575e+15	Mean :5.861e+16	Mean : 1.043	Mean : 0.320	Mean : 3.088	Mean : 0.100	Mean : 0.068	Mean : 0.928	Mean :0.004	Mean :0.000	Mean :0.00	Mean :0.000	Mean :0.000	Mean :0	Mean : 5042	Mean :0.236	Mean :0.417	Mean :0.518	Mean :10.758	Mean : 5.557	Mean : 3.458	Mean : 1416666	Mean :1.856e+12	Mean :1.666e+18	Mean :2.191e+15	Mean : 0.780	Mean : 0.153	Mean :0.007	Mean : 0.434	Mean :1.652e+09	Mean :5.106e+18	Mean :4.232e+19	Mean :1.487e+15	Mean :1.019e+15	Mean :1.456e+16	Mean : 1.669	Mean : 1.234	Mean : 19.76	Mean : 0.334	Mean : 0.268	Mean : 2.430	Mean :0.009	Mean :0.000	Mean : 0.002	Mean :0.00	Mean :0.000	Mean :0.000	Mean : 3800	Mean :0.240	Mean :0.418	Mean :0.522	Mean :10.377	Mean : 5.298	Mean : 3.279	Mean : 17.299	Mean : 21.4	Mean :0.6916	Mean : 1793267	Mean :4.402e+12	Mean :2.929e+19	Mean :6.519e+15	Mean :0.689	Mean :0.113	Mean :0.004	Mean : 0.191	Mean :2.154e+09	Mean :2.754e+19	Mean :1.289e+20	Mean :4.253e+15	Mean :2.743e+15	Mean :3.912e+16	Mean : 1.176	Mean : 0.461	Mean : 5.020	Mean : 0.136	Mean : 0.099	Mean : 1.241	Mean :0.004	Mean :0.000	Mean :0.000	Mean :0.000	Mean :0.000	Mean :0	Mean : 4477	Mean :0.242	Mean :0.422	Mean :0.522	Mean :10.534	Mean : 5.386	Mean : 3.353	Mean : 1256622	Mean :1.367e+12	Mean :1.039e+18	Mean :1.695e+15	Mean : 0.771	Mean : 0.148	Mean :0.007	Mean : 0.541	Mean :1.425e+09	Mean :3.628e+18	Mean :3.046e+19	Mean :1.206e+15	Mean :8.804e+14	Mean :1.084e+16	Mean : 1.688	Mean : 1.227	Mean : 25.74	Mean : 0.456	Mean : 0.400	Mean : 2.607	Mean :0.008	Mean :0.000	Mean :0.001	Mean :0.000	Mean :0.000	Mean :0.000	Mean : 3618.9	Mean :0.246	Mean :0.423	Mean :0.526	Mean :10.167	Mean : 5.141	Mean : 3.188	Mean : 13.862	Mean : 16.100	Mean :0.59	Mean : 1489327	Mean :2.966e+12	Mean :1.528e+19	Mean :4.724e+15	Mean :0.699	Mean :0.116	Mean :0.004	Mean : 0.253	Mean :1.726e+09	Mean :1.838e+19	Mean :8.501e+19	Mean :3.188e+15	Mean :2.164e+15	Mean :2.655e+16	Mean : 1.268	Mean : 0.495	Mean : 7.081	Mean : 0.193	Mean : 0.153	Mean : 1.495	Mean :0.005	Mean :0.000	Mean :0.000	Mean :0.000	Mean :0.000	Mean :0	Mean : 4002	Mean :0.251	Mean :0.427	Mean :0.532	Mean :10.218	Mean : 5.179	Mean : 3.254	Mean : 1092621	Mean :9.870e+11	Mean :6.227e+17	Mean :1.339e+15	Mean : 0.753	Mean :0.141	Mean :0.006	Mean : 1.045	Mean :1.196e+09	Mean :2.495e+18	Mean :2.148e+19	Mean :9.911e+14	Mean :7.593e+14	Mean :7.930e+15	Mean : 1.737	Mean : 0.956	Mean : 73.8	Mean : 0.958	Mean : 0.900	Mean : 3.705	Mean :0.009	Mean :0.000	Mean : 0.009	Mean :0.001	Mean :0.001	Mean :0.000	Mean : 3446.0	Mean :0.256	Mean :0.428	Mean :0.537	Mean : 9.854	Mean : 4.944	Mean : 3.097	Mean : 10.88	Mean : 11.94	Mean :0.4852	Mean : 1222842	Mean :2.019e+12	Mean :8.302e+18	Mean :3.378e+15	Mean : 0.687	Mean :0.112	Mean :0.004	Mean : 0.278	Mean :1.350e+09	Mean :1.269e+19	Mean :5.687e+19	Mean :2.295e+15	Mean :1.573e+15	Mean :1.828e+16	Mean : 1.299	Mean : 1.250	Mean : 12.227	Mean : 0.224	Mean : 0.187	Mean : 1.879	Mean :0.005	Mean :0.000	Mean :0.000	Mean :0.00	Mean :0.000	Mean :0	Mean : 3606	Mean :0.266	Mean :0.437	Mean :0.550	Mean : 9.751	Mean : 4.941	Mean : 3.170	Mean : 926558	Mean :6.708e+11	Mean :3.182e+17	Mean :9.803e+14	Mean : 0.717	Mean :0.129	Mean :0.005	Mean : 0.478	Mean :9.550e+08	Mean :1.601e+18	Mean :1.355e+19	Mean :7.529e+14	Mean :6.013e+14	Mean :5.339e+15	Mean : 1.628	Mean : 3.869	Mean : 31.36	Mean : 0.415	Mean : 0.372	Mean : 2.996	Mean :0.008	Mean :0.000	Mean :0.002	Mean :0.000	Mean :0.00	Mean :0.000	Mean : 3285.4	Mean :0.271	Mean :0.439	Mean :0.555	Mean : 9.395	Mean : 4.716	Mean : 3.018	Mean : 8.499	Mean : 8.825	Mean :0.3972	Mean : 980603	Mean :1.345e+12	Mean :4.072e+18	Mean :2.389e+15	Mean :0.650	Mean :0.103	Mean :0.004	Mean : 0.315	Mean :1.037e+09	Mean :8.459e+18	Mean :3.897e+19	Mean :1.649e+15	Mean :1.156e+15	Mean :1.262e+16	Mean : 1.182	Mean : 0.447	Mean : 9.429	Mean : 0.263	Mean : 0.229	Mean : 1.570	Mean :0.005	Mean :0.000	Mean :0.000	Mean :0.000	Mean :0.000	Mean :0	Mean : 3254	Mean :0.285	Mean :0.451	Mean :0.577	Mean : 9.133	Mean : 4.684	Mean : 3.096	Mean : 761473	Mean :4.467e+11	Mean :1.706e+17	Mean :6.972e+14	Mean :0.659	Mean :0.114	Mean :0.005	Mean : 0.406	Mean :7.320e+08	Mean :1.082e+18	Mean :9.105e+18	Mean :5.415e+14	Mean :4.377e+14	Mean :3.615e+15	Mean : 1.407	Mean : 0.689	Mean : 11.904	Mean : 0.343	Mean : 0.300	Mean : 2.001	Mean :0.007	Mean :0.000	Mean :0.000	Mean :0.000	Mean :0.000	Mean :0	Mean : 3133.6	Mean :0.290	Mean :0.453	Mean :0.582	Mean : 8.797	Mean : 4.464	Mean : 2.948	Mean : 6.594	Mean : 6.491	Mean :0.3167	Mean : 770168	Mean :8.672e+11	Mean :1.746e+18	Mean :1.586e+15	Mean :0.599	Mean :0.088	Mean :0.003	Mean : 0.201	Mean :7.658e+08	Mean :5.362e+18	Mean :2.466e+19	Mean :1.111e+15	Mean :7.948e+14	Mean :8.283e+15	Mean : 0.996	Mean : 0.314	Mean : 5.072	Mean : 0.157	Mean : 0.128	Mean : 1.152	Mean :0.004	Mean :0.000	Mean :0.000	Mean :0.000	Mean :0.000	Mean :0	Mean : 2951	Mean :0.302	Mean :0.462	Mean :0.604	Mean : 8.469	Mean : 4.391	Mean : 3.013	Mean : 607846	Mean :2.916e+11	Mean :8.687e+16	Mean :4.451e+14	Mean :0.596	Mean :0.097	Mean :0.004	Mean : 0.321	Mean :5.321e+08	Mean :7.080e+17	Mean :5.720e+18	Mean :3.426e+14	Mean :2.742e+14	Mean :2.388e+15	Mean : 1.205	Mean : 0.627	Mean : 8.923	Mean : 0.262	Mean : 0.223	Mean : 1.636	Mean :0.006	Mean :0.000	Mean :0.000	Mean :0.000	Mean :0.00	Mean :0	Mean : 2997.7	Mean :0.306	Mean :0.463	Mean :0.610	Mean : 8.159	Mean : 4.179	Mean : 2.866	Mean : 5.125	Mean : 4.805	Mean :0.2491	Mean : 615842	Mean :5.264e+11	Mean :5.428e+17	Mean :1.288e+15	Mean :0.564	Mean :0.077	Mean :0.003	Mean : 0.197	Mean :5.785e+08	Mean :3.064e+18	Mean :1.607e+19	Mean :8.853e+14	Mean :6.166e+14	Mean :5.382e+15	Mean : 0.908	Mean : 0.327	Mean : 6.447	Mean : 0.167	Mean : 0.147	Mean : 1.216	Mean :0.004	Mean :0.000	Mean :0.000	Mean :0.000	Mean :0.00	Mean :0	Mean : 2720	Mean :0.314	Mean :0.464	Mean :0.631	Mean : 7.959	Mean : 4.151	Mean : 2.954	Mean : 498566	Mean :1.960e+11	Mean :4.007e+16	Mean :3.507e+14	Mean : 0.550	Mean : 0.085	Mean :0.004	Mean : 0.349	Mean :4.013e+08	Mean :4.416e+17	Mean :4.001e+18	Mean :2.741e+14	Mean :2.230e+14	Mean :1.661e+15	Mean : 1.116	Mean : 1.051	Mean : 14.992	Mean : 0.297	Mean : 0.262	Mean : 1.927	Mean :0.006	Mean :0.000	Mean :0.001	Mean :0.000	Mean :0.000	Mean :0.000	Mean : 2900.9	Mean :0.318	Mean :0.465	Mean :0.637	Mean : 7.671	Mean : 3.948	Mean : 2.810	Mean : 937.24	Mean : 31.535	Mean :0.04484	Mean :0.1815	Mean :0	Mean : 1.690	Mean :0.3039	Mean :0.9725	NA	NA	NA	Mean :0.2	Mean :1.9	Mean :1.4	Mean :2	Mean :2.128	Mean :-7.000e-11	Mean :0.275025	Mean :0.1927164	Mean :-0.9678	Mean : 2.7854	Mean : 2.942e-09	Mean :0.26458	Mean :0.197404	Mean :-0.8724	Mean : 2.5077	Mean :-5.264e-10	Mean :0.133948	Mean :0.0575110	Mean : -0.75040	Mean : 2.38074	Mean : 4.934e-10	Mean :0.247163	Mean :0.182585	Mean :-0.7575	Mean : 2.4045	Mean : 1615555	Mean : 745313	Mean : 1369266	Mean :0.3389	Mean :-0.00593	Mean :0.11494	Mean :-0.1417209	Mean :0.19472	Mean :8.550e-02	Mean :0.35322	Mean :-0.00488	Mean :0.07565	Mean :-0.13486	Mean :0.16813	Mean : 0.081655	Mean :0.35238	Mean :-0.00190	Mean :0.11144	Mean :-0.018502	Mean :0.13226	Mean : 0.01071	Mean :0.14801	Mean :-0.00403	Mean :0.04446	Mean :-0.123219	Mean :0.14792	Mean : 0.074781	Mean :0.33536	Mean :-0.28451	Mean :0.15577	Mean :1.348e+73	Mean :6.825e+71	Mean : 0.4472	Mean :-0.00524	Mean :0.11325	Mean :2.5	Mean :7.1	Mean :0.5
	123l BME 901 A: 1	3a0b : 11	MG : 402	201 : 306	E : 221	3rd Qu.: NA	3rd Qu.: NA	3rd Qu.: NA	3rd Qu.: NA	3rd Qu.: NA	3rd Qu.: NA	3rd Qu.: NA	3rd Qu.: NA	3rd Qu.: NA	3rd Qu.: NA	3rd Qu.: NA	3rd Qu.: 21.00	3rd Qu.: 20.00	3rd Qu.: 20.00	3rd Qu.:145.0	3rd Qu.:139.15	3rd Qu.:10.000	3rd Qu.: 2.00	3rd Qu.: 5.000	3rd Qu.:0.0000	3rd Qu.: 21.00	3rd Qu.:151.0	3rd Qu.:10.00	3rd Qu.: 2.000	3rd Qu.: 6.000	3rd Qu.:0.0000	3rd Qu.: 41.434	3rd Qu.: 67.06	3rd Qu.:1.0000	3rd Qu.: 2563883	3rd Qu.:1.258e+12	3rd Qu.:1.241e+17	3rd Qu.:2.287e+14	3rd Qu.:0.7365	3rd Qu.:0.0992	3rd Qu.:0.0028	3rd Qu.:0.0521	3rd Qu.:1.197e+09	3rd Qu.:1.535e+17	3rd Qu.:7.688e+17	3rd Qu.:1.300e+14	3rd Qu.:5.116e+13	3rd Qu.:1.647e+15	3rd Qu.: 0.8471	3rd Qu.: 0.0720	3rd Qu.: 0.4041	3rd Qu.:0.0306	3rd Qu.:0.0150	3rd Qu.: 0.3408	3rd Qu.:0.0026	3rd Qu.:0.0000	3rd Qu.:0.0000	3rd Qu.:0.0000	3rd Qu.:0e+00	3rd Qu.:0	3rd Qu.: 8604	3rd Qu.:0.2864	3rd Qu.:0.5983	3rd Qu.:0.7264	3rd Qu.:14.739	3rd Qu.: 7.444	3rd Qu.: 4.313	3rd Qu.: 1405891	3rd Qu.:3.835e+11	3rd Qu.:2.108e+16	3rd Qu.:7.100e+13	3rd Qu.:1.0132	3rd Qu.:0.1898	3rd Qu.:0.0074	3rd Qu.: 0.1622	3rd Qu.:6.281e+08	3rd Qu.:4.085e+16	3rd Qu.:2.124e+17	3rd Qu.:4.662e+13	3rd Qu.:2.294e+13	3rd Qu.:4.635e+14	3rd Qu.: 1.6894	3rd Qu.: 0.2874	3rd Qu.: 1.6383	3rd Qu.: 0.1056	3rd Qu.: 0.0611	3rd Qu.: 0.9441	3rd Qu.:0.0077	3rd Qu.:0.0000	3rd Qu.:0.0000	3rd Qu.:0.0000	3rd Qu.:0.0000	3rd Qu.:0.0000	3rd Qu.: 5322	3rd Qu.:0.3017	3rd Qu.:0.6051	3rd Qu.:0.7363	3rd Qu.:14.368	3rd Qu.: 7.143	3rd Qu.: 4.0015	3rd Qu.: 39.384	3rd Qu.: 61.21	3rd Qu.:1.0000	3rd Qu.: 2444647	3rd Qu.:1.136e+12	3rd Qu.:1.036e+17	3rd Qu.:2.211e+14	3rd Qu.:0.7633	3rd Qu.:0.1046	3rd Qu.:0.0029	3rd Qu.:0.0576	3rd Qu.:1.145e+09	3rd Qu.:1.387e+17	3rd Qu.:7.039e+17	3rd Qu.:1.258e+14	3rd Qu.:4.840e+13	3rd Qu.:1.513e+15	3rd Qu.: 0.9138	3rd Qu.: 0.0810	3rd Qu.: 0.4712	3rd Qu.:0.0344	3rd Qu.:0.0168	3rd Qu.: 0.3852	3rd Qu.:0.0029	3rd Qu.:0.0000	3rd Qu.:0.000	3rd Qu.:0.0000	3rd Qu.:0.000	3rd Qu.:0	3rd Qu.: 8213	3rd Qu.:0.2989	3rd Qu.:0.6085	3rd Qu.:0.7338	3rd Qu.:14.839	3rd Qu.: 7.393	3rd Qu.: 4.2305	3rd Qu.: 1411425	3rd Qu.:3.754e+11	3rd Qu.:1.981e+16	3rd Qu.:7.614e+13	3rd Qu.:1.0250	3rd Qu.:0.1896	3rd Qu.:0.0073	3rd Qu.: 0.1652	3rd Qu.:6.229e+08	3rd Qu.:4.167e+16	3rd Qu.:2.155e+17	3rd Qu.:4.987e+13	3rd Qu.:2.406e+13	3rd Qu.:4.685e+14	3rd Qu.: 1.7199	3rd Qu.: 0.2869	3rd Qu.: 1.696	3rd Qu.: 0.1089	3rd Qu.: 0.0639	3rd Qu.: 0.9822	3rd Qu.:0.0078	3rd Qu.:0.0000	3rd Qu.:0.0000	3rd Qu.:0.0000	3rd Qu.:0.0000	3rd Qu.:0.0000	3rd Qu.: 5277.11	3rd Qu.:0.3139	3rd Qu.:0.6152	3rd Qu.:0.7444	3rd Qu.:14.466	3rd Qu.: 7.099	3rd Qu.: 3.9472	3rd Qu.: 34.14	3rd Qu.: 47.706	3rd Qu.:1.0000	3rd Qu.: 2198054	3rd Qu.:9.254e+11	3rd Qu.:7.874e+16	3rd Qu.:1.902e+14	3rd Qu.:0.8274	3rd Qu.:0.1201	3rd Qu.:0.0033	3rd Qu.: 0.0759	3rd Qu.:1.030e+09	3rd Qu.:1.117e+17	3rd Qu.:5.691e+17	3rd Qu.:1.103e+14	3rd Qu.:4.242e+13	3rd Qu.:1.216e+15	3rd Qu.: 1.0766	3rd Qu.: 0.1077	3rd Qu.: 0.6657	3rd Qu.: 0.0462	3rd Qu.: 0.0222	3rd Qu.: 0.5005	3rd Qu.:0.0035	3rd Qu.:0.0000	3rd Qu.:0.0000	3rd Qu.:0.0000	3rd Qu.:0.0000	3rd Qu.:0	3rd Qu.: 7378	3rd Qu.:0.3368	3rd Qu.:0.6307	3rd Qu.:0.7479	3rd Qu.:15.036	3rd Qu.: 7.290	3rd Qu.: 4.1043	3rd Qu.: 1379860	3rd Qu.:3.597e+11	3rd Qu.:1.752e+16	3rd Qu.:8.023e+13	3rd Qu.:1.0468	3rd Qu.:0.1909	3rd Qu.:0.0069	3rd Qu.: 0.1823	3rd Qu.:6.298e+08	3rd Qu.:3.960e+16	3rd Qu.:2.164e+17	3rd Qu.:5.226e+13	3rd Qu.:2.598e+13	3rd Qu.:4.794e+14	3rd Qu.: 1.7974	3rd Qu.: 0.3002	3rd Qu.: 1.901	3rd Qu.: 0.1243	3rd Qu.: 0.0725	3rd Qu.: 1.0608	3rd Qu.:0.0079	3rd Qu.:0.0000	3rd Qu.:0.0000	3rd Qu.:0.0000	3rd Qu.:0.0000	3rd Qu.:0.0000	3rd Qu.: 5181.2	3rd Qu.:0.3548	3rd Qu.:0.6380	3rd Qu.:0.7573	3rd Qu.:14.648	3rd Qu.: 7.018	3rd Qu.: 3.8449	3rd Qu.: 28.5	3rd Qu.: 36.67	3rd Qu.:1.0000	3rd Qu.: 1985591	3rd Qu.:7.154e+11	3rd Qu.:4.886e+16	3rd Qu.:1.600e+14	3rd Qu.:0.884	3rd Qu.:0.134	3rd Qu.:0.004	3rd Qu.: 0.097	3rd Qu.:9.385e+08	3rd Qu.:8.713e+16	3rd Qu.:5.017e+17	3rd Qu.:9.193e+13	3rd Qu.:3.750e+13	3rd Qu.:1.022e+15	3rd Qu.: 1.213	3rd Qu.: 0.133	3rd Qu.: 0.881	3rd Qu.: 0.058	3rd Qu.: 0.029	3rd Qu.: 0.610	3rd Qu.:0.004	3rd Qu.:0.000	3rd Qu.:0.00	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.:0	3rd Qu.: 6578	3rd Qu.:0.378	3rd Qu.:0.659	3rd Qu.:0.762	3rd Qu.:15.087	3rd Qu.: 7.121	3rd Qu.: 3.971	3rd Qu.: 1320446	3rd Qu.:3.169e+11	3rd Qu.:1.409e+16	3rd Qu.:7.506e+13	3rd Qu.: 1.068	3rd Qu.: 0.189	3rd Qu.:0.007	3rd Qu.: 0.193	3rd Qu.:6.024e+08	3rd Qu.:3.541e+16	3rd Qu.:2.111e+17	3rd Qu.:4.923e+13	3rd Qu.:2.523e+13	3rd Qu.:4.485e+14	3rd Qu.: 1.859	3rd Qu.: 0.305	3rd Qu.: 2.03	3rd Qu.: 0.132	3rd Qu.: 0.079	3rd Qu.: 1.123	3rd Qu.:0.008	3rd Qu.:0.000	3rd Qu.: 0.000	3rd Qu.:0.00	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.: 5018	3rd Qu.:0.400	3rd Qu.:0.667	3rd Qu.:0.773	3rd Qu.:14.673	3rd Qu.: 6.898	3rd Qu.: 3.738	3rd Qu.: 23.645	3rd Qu.: 27.2	3rd Qu.:1.0000	3rd Qu.: 1660848	3rd Qu.:4.777e+11	3rd Qu.:2.560e+16	3rd Qu.:9.978e+13	3rd Qu.:0.913	3rd Qu.:0.139	3rd Qu.:0.004	3rd Qu.: 0.109	3rd Qu.:7.484e+08	3rd Qu.:5.254e+16	3rd Qu.:3.179e+17	3rd Qu.:6.074e+13	3rd Qu.:2.617e+13	3rd Qu.:6.772e+14	3rd Qu.: 1.309	3rd Qu.: 0.153	3rd Qu.: 1.000	3rd Qu.: 0.069	3rd Qu.: 0.035	3rd Qu.: 0.675	3rd Qu.:0.005	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.:0	3rd Qu.: 5715	3rd Qu.:0.414	3rd Qu.:0.674	3rd Qu.:0.778	3rd Qu.:14.650	3rd Qu.: 6.874	3rd Qu.: 3.811	3rd Qu.: 1199231	3rd Qu.:2.535e+11	3rd Qu.:9.606e+15	3rd Qu.:5.416e+13	3rd Qu.: 1.080	3rd Qu.: 0.185	3rd Qu.:0.006	3rd Qu.: 0.198	3rd Qu.:5.172e+08	3rd Qu.:2.475e+16	3rd Qu.:1.516e+17	3rd Qu.:3.561e+13	3rd Qu.:1.979e+13	3rd Qu.:3.399e+14	3rd Qu.: 1.916	3rd Qu.: 0.292	3rd Qu.: 2.22	3rd Qu.: 0.136	3rd Qu.: 0.085	3rd Qu.: 1.187	3rd Qu.:0.008	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.: 4716.6	3rd Qu.:0.431	3rd Qu.:0.681	3rd Qu.:0.785	3rd Qu.:14.332	3rd Qu.: 6.642	3rd Qu.: 3.601	3rd Qu.: 19.464	3rd Qu.: 19.824	3rd Qu.:1.00	3rd Qu.: 1371273	3rd Qu.:3.140e+11	3rd Qu.:1.314e+16	3rd Qu.:5.502e+13	3rd Qu.:0.921	3rd Qu.:0.139	3rd Qu.:0.004	3rd Qu.: 0.116	3rd Qu.:5.617e+08	3rd Qu.:2.766e+16	3rd Qu.:1.793e+17	3rd Qu.:3.384e+13	3rd Qu.:1.651e+13	3rd Qu.:4.296e+14	3rd Qu.: 1.350	3rd Qu.: 0.161	3rd Qu.: 1.109	3rd Qu.: 0.074	3rd Qu.: 0.039	3rd Qu.: 0.710	3rd Qu.:0.005	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.:0	3rd Qu.: 5064	3rd Qu.:0.455	3rd Qu.:0.694	3rd Qu.:0.791	3rd Qu.:14.138	3rd Qu.: 6.550	3rd Qu.: 3.655	3rd Qu.: 1031112	3rd Qu.:1.790e+11	3rd Qu.:5.742e+15	3rd Qu.:3.455e+13	3rd Qu.: 1.052	3rd Qu.:0.176	3rd Qu.:0.006	3rd Qu.: 0.191	3rd Qu.:3.961e+08	3rd Qu.:1.451e+16	3rd Qu.:9.160e+16	3rd Qu.:2.328e+13	3rd Qu.:1.350e+13	3rd Qu.:2.183e+14	3rd Qu.: 1.817	3rd Qu.: 0.266	3rd Qu.: 2.1	3rd Qu.: 0.136	3rd Qu.: 0.089	3rd Qu.: 1.124	3rd Qu.:0.008	3rd Qu.:0.000	3rd Qu.: 0.000	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.: 4449.9	3rd Qu.:0.468	3rd Qu.:0.697	3rd Qu.:0.799	3rd Qu.:13.871	3rd Qu.: 6.336	3rd Qu.: 3.466	3rd Qu.: 15.80	3rd Qu.: 14.36	3rd Qu.:1.0000	3rd Qu.: 1107271	3rd Qu.:2.027e+11	3rd Qu.:7.000e+15	3rd Qu.:3.081e+13	3rd Qu.: 0.883	3rd Qu.:0.130	3rd Qu.:0.003	3rd Qu.: 0.105	3rd Qu.:3.914e+08	3rd Qu.:1.424e+16	3rd Qu.:8.976e+16	3rd Qu.:1.922e+13	3rd Qu.:9.001e+12	3rd Qu.:2.430e+14	3rd Qu.: 1.261	3rd Qu.: 0.133	3rd Qu.: 0.979	3rd Qu.: 0.068	3rd Qu.: 0.038	3rd Qu.: 0.652	3rd Qu.:0.005	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.:0.00	3rd Qu.:0.000	3rd Qu.:0	3rd Qu.: 4507	3rd Qu.:0.495	3rd Qu.:0.715	3rd Qu.:0.808	3rd Qu.:13.334	3rd Qu.: 6.186	3rd Qu.: 3.528	3rd Qu.: 866913	3rd Qu.:1.323e+11	3rd Qu.:3.651e+15	3rd Qu.:2.025e+13	3rd Qu.: 1.002	3rd Qu.:0.162	3rd Qu.:0.005	3rd Qu.: 0.171	3rd Qu.:2.906e+08	3rd Qu.:8.087e+15	3rd Qu.:4.935e+16	3rd Qu.:1.365e+13	3rd Qu.:7.566e+12	3rd Qu.:1.366e+14	3rd Qu.: 1.708	3rd Qu.: 0.224	3rd Qu.: 1.79	3rd Qu.: 0.129	3rd Qu.: 0.076	3rd Qu.: 0.981	3rd Qu.:0.008	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.:0.00	3rd Qu.:0.000	3rd Qu.: 4279.1	3rd Qu.:0.511	3rd Qu.:0.721	3rd Qu.:0.816	3rd Qu.:13.031	3rd Qu.: 5.983	3rd Qu.: 3.340	3rd Qu.: 12.412	3rd Qu.: 10.976	3rd Qu.:1.0000	3rd Qu.: 814855	3rd Qu.:1.176e+11	3rd Qu.:3.385e+15	3rd Qu.:1.206e+13	3rd Qu.:0.801	3rd Qu.:0.114	3rd Qu.:0.003	3rd Qu.: 0.080	3rd Qu.:2.398e+08	3rd Qu.:5.767e+15	3rd Qu.:3.260e+16	3rd Qu.:7.591e+12	3rd Qu.:3.179e+12	3rd Qu.:1.042e+14	3rd Qu.: 1.050	3rd Qu.: 0.095	3rd Qu.: 0.639	3rd Qu.: 0.053	3rd Qu.: 0.026	3rd Qu.: 0.471	3rd Qu.:0.005	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.:0	3rd Qu.: 4013	3rd Qu.:0.537	3rd Qu.:0.740	3rd Qu.:0.826	3rd Qu.:11.845	3rd Qu.: 5.824	3rd Qu.: 3.387	3rd Qu.: 690176	3rd Qu.:8.212e+10	3rd Qu.:1.911e+15	3rd Qu.:9.179e+12	3rd Qu.:0.903	3rd Qu.:0.139	3rd Qu.:0.004	3rd Qu.: 0.127	3rd Qu.:1.923e+08	3rd Qu.:3.656e+15	3rd Qu.:2.131e+16	3rd Qu.:5.996e+12	3rd Qu.:3.316e+12	3rd Qu.:6.995e+13	3rd Qu.: 1.407	3rd Qu.: 0.158	3rd Qu.: 1.187	3rd Qu.: 0.091	3rd Qu.: 0.054	3rd Qu.: 0.741	3rd Qu.:0.007	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.:0	3rd Qu.: 4036.0	3rd Qu.:0.554	3rd Qu.:0.747	3rd Qu.:0.834	3rd Qu.:11.634	3rd Qu.: 5.615	3rd Qu.: 3.204	3rd Qu.: 8.015	3rd Qu.: 8.848	3rd Qu.:1.0000	3rd Qu.: 572611	3rd Qu.:6.305e+10	3rd Qu.:1.417e+15	3rd Qu.:4.757e+12	3rd Qu.:0.703	3rd Qu.:0.089	3rd Qu.:0.002	3rd Qu.: 0.052	3rd Qu.:1.303e+08	3rd Qu.:1.804e+15	3rd Qu.:8.163e+15	3rd Qu.:2.646e+12	3rd Qu.:1.042e+12	3rd Qu.:3.926e+13	3rd Qu.: 0.809	3rd Qu.: 0.056	3rd Qu.: 0.372	3rd Qu.: 0.032	3rd Qu.: 0.017	3rd Qu.: 0.319	3rd Qu.:0.004	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.:0	3rd Qu.: 3556	3rd Qu.:0.566	3rd Qu.:0.758	3rd Qu.:0.838	3rd Qu.:10.520	3rd Qu.: 5.438	3rd Qu.: 3.241	3rd Qu.: 522346	3rd Qu.:4.874e+10	3rd Qu.:9.783e+14	3rd Qu.:4.184e+12	3rd Qu.:0.754	3rd Qu.:0.099	3rd Qu.:0.003	3rd Qu.: 0.065	3rd Qu.:1.118e+08	3rd Qu.:1.395e+15	3rd Qu.:5.903e+15	3rd Qu.:2.501e+12	3rd Qu.:1.071e+12	3rd Qu.:2.916e+13	3rd Qu.: 0.982	3rd Qu.: 0.077	3rd Qu.: 0.550	3rd Qu.: 0.047	3rd Qu.: 0.026	3rd Qu.: 0.410	3rd Qu.:0.005	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.:0.00	3rd Qu.:0	3rd Qu.: 3776.1	3rd Qu.:0.584	3rd Qu.:0.761	3rd Qu.:0.846	3rd Qu.:10.263	3rd Qu.: 5.200	3rd Qu.: 3.063	3rd Qu.: 0.000	3rd Qu.: 0.000	3rd Qu.:0.0000	3rd Qu.: 407467	3rd Qu.:3.338e+10	3rd Qu.:6.203e+14	3rd Qu.:2.083e+12	3rd Qu.:0.620	3rd Qu.:0.072	3rd Qu.:0.002	3rd Qu.: 0.033	3rd Qu.:7.534e+07	3rd Qu.:6.485e+14	3rd Qu.:2.948e+15	3rd Qu.:1.143e+12	3rd Qu.:3.898e+11	3rd Qu.:1.651e+13	3rd Qu.: 0.593	3rd Qu.: 0.036	3rd Qu.: 0.198	3rd Qu.: 0.019	3rd Qu.: 0.009	3rd Qu.: 0.198	3rd Qu.:0.003	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.:0.00	3rd Qu.:0	3rd Qu.: 3176	3rd Qu.:0.586	3rd Qu.:0.769	3rd Qu.:0.853	3rd Qu.: 9.419	3rd Qu.: 4.989	3rd Qu.: 3.140	3rd Qu.: 389669	3rd Qu.:3.000e+10	3rd Qu.:4.772e+14	3rd Qu.:1.779e+12	3rd Qu.: 0.608	3rd Qu.: 0.074	3rd Qu.:0.002	3rd Qu.: 0.032	3rd Qu.:6.932e+07	3rd Qu.:5.867e+14	3rd Qu.:2.249e+15	3rd Qu.:1.103e+12	3rd Qu.:4.860e+11	3rd Qu.:1.376e+13	3rd Qu.: 0.583	3rd Qu.: 0.038	3rd Qu.: 0.192	3rd Qu.: 0.020	3rd Qu.: 0.011	3rd Qu.: 0.195	3rd Qu.:0.004	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.:0.000	3rd Qu.: 3583.6	3rd Qu.:0.599	3rd Qu.:0.773	3rd Qu.:0.859	3rd Qu.: 9.171	3rd Qu.: 4.796	3rd Qu.: 2.962	3rd Qu.: 1140.48	3rd Qu.: 41.434	3rd Qu.:0.05407	3rd Qu.:0.2123	3rd Qu.:0	3rd Qu.: 2.098	3rd Qu.:0.3560	3rd Qu.:1.0000	NA	NA	NA	3rd Qu.:0.2	3rd Qu.:1.9	3rd Qu.:1.4	3rd Qu.:2	3rd Qu.:2.401	3rd Qu.: 4.709e-10	3rd Qu.:0.337722	3rd Qu.:0.2628045	3rd Qu.:-0.7773	3rd Qu.: 3.4233	3rd Qu.: 6.750e-10	3rd Qu.:0.32442	3rd Qu.:0.269623	3rd Qu.:-0.6866	3rd Qu.: 3.1626	3rd Qu.: 1.301e-10	3rd Qu.:0.164254	3rd Qu.:0.0715205	3rd Qu.: -0.55303	3rd Qu.: 2.95817	3rd Qu.: 4.920e-10	3rd Qu.:0.304156	3rd Qu.:0.251224	3rd Qu.:-0.5877	3rd Qu.: 3.0455	3rd Qu.: 1632553	3rd Qu.: 847645	3rd Qu.: 1577760	3rd Qu.:0.4363	3rd Qu.:-0.00172	3rd Qu.:0.14024	3rd Qu.:-0.1177243	3rd Qu.:0.22674	3rd Qu.:9.420e-02	3rd Qu.:0.42618	3rd Qu.:-0.00113	3rd Qu.:0.09062	3rd Qu.:-0.11395	3rd Qu.:0.19424	3rd Qu.: 0.091062	3rd Qu.:0.42462	3rd Qu.:-0.00012	3rd Qu.:0.13623	3rd Qu.:-0.007293	3rd Qu.:0.15861	3rd Qu.: 0.01579	3rd Qu.:0.17967	3rd Qu.:-0.00043	3rd Qu.:0.05314	3rd Qu.:-0.106279	3rd Qu.:0.17112	3rd Qu.: 0.084687	3rd Qu.:0.40525	3rd Qu.:-0.23695	3rd Qu.:0.17675	3rd Qu.:0.000e+00	3rd Qu.:0.000e+00	3rd Qu.: 0.5041	3rd Qu.:-0.00096	3rd Qu.:0.13851	3rd Qu.:2.5	3rd Qu.:7.1	3rd Qu.:0.5
	123l CL 173 A : 1	3g6e : 11	CL : 400	500 : 228	H : 153	Max. : NA	Max. : NA	Max. : NA	Max. : NA	Max. : NA	Max. : NA	Max. : NA	Max. : NA	Max. : NA	Max. : NA	Max. : NA	Max. :178.00	Max. :104.00	Max. :104.00	Max. :800.0	Max. :800.00	Max. :81.000	Max. :15.00	Max. :55.000	Max. :9.0000	Max. :128.00	Max. :866.0	Max. :81.00	Max. :20.000	Max. :55.000	Max. :9.0000	Max. :320.010	Max. :1467.68	Max. :3.0000	Max. :531191023	Max. :6.478e+16	Max. :2.263e+24	Max. :1.007e+20	Max. :2.8594	Max. :0.8164	Max. :0.0560	Max. :7.4135	Max. :2.280e+12	Max. :9.980e+23	Max. :3.152e+24	Max. :5.832e+19	Max. :3.005e+19	Max. :5.869e+20	Max. :14.2814	Max. :44.5807	Max. :179.6831	Max. :4.1117	Max. :3.5597	Max. :32.0411	Max. :0.0778	Max. :0.0014	Max. :0.0056	Max. :0.0014	Max. :8e-04	Max. :0	Max. :183460	Max. :0.9517	Max. :0.9949	Max. :1.0000	Max. :57.618	Max. :23.513	Max. :19.245	Max. :103498612	Max. :2.493e+15	Max. :1.713e+22	Max. :4.246e+18	Max. :6.2210	Max. :3.2789	Max. :0.4408	Max. :56.1583	Max. :5.444e+11	Max. :3.703e+22	Max. :2.191e+23	Max. :2.480e+18	Max. :1.500e+18	Max. :3.396e+19	Max. :55.3711	Max. :671.2784	Max. :2712.8508	Max. :54.3712	Max. :53.1798	Max. :257.4787	Max. :1.0142	Max. :0.0892	Max. :0.9735	Max. :0.1136	Max. :0.1108	Max. :0.0036	Max. :40001	Max. :0.9577	Max. :0.9973	Max. :1.0000	Max. :57.158	Max. :23.337	Max. :18.8283	Max. :308.854	Max. :1364.02	Max. :3.0000	Max. :488904295	Max. :5.504e+16	Max. :1.774e+24	Max. :8.822e+19	Max. :3.2426	Max. :0.8686	Max. :0.0658	Max. :8.7118	Max. :2.080e+12	Max. :8.316e+23	Max. :2.328e+24	Max. :5.101e+19	Max. :2.620e+19	Max. :4.918e+20	Max. :15.7489	Max. :17.9810	Max. :216.0913	Max. :5.7801	Max. :4.1458	Max. :35.0370	Max. :0.0797	Max. :0.0012	Max. :0.005	Max. :0.0016	Max. :0.001	Max. :0	Max. :170502	Max. :0.9550	Max. :0.9933	Max. :1.0000	Max. :57.498	Max. :23.530	Max. :19.1324	Max. :98387452	Max. :2.257e+15	Max. :1.476e+22	Max. :3.925e+18	Max. :6.8569	Max. :3.2948	Max. :0.4961	Max. :71.1604	Max. :4.837e+11	Max. :3.307e+22	Max. :1.741e+23	Max. :2.292e+18	Max. :1.423e+18	Max. :2.699e+19	Max. :69.6222	Max. :249.4129	Max. :4234.377	Max. :69.3275	Max. :68.1055	Max. :353.0231	Max. :0.6962	Max. :0.1087	Max. :0.3068	Max. :0.0309	Max. :0.0301	Max. :0.0052	Max. :38606.79	Max. :0.9601	Max. :0.9942	Max. :1.0000	Max. :56.999	Max. :23.302	Max. :18.7365	Max. :285.05	Max. :1126.792	Max. :6.0000	Max. :393595202	Max. :3.603e+16	Max. :9.429e+23	Max. :6.152e+19	Max. :4.1657	Max. :1.2761	Max. :0.1188	Max. :15.0771	Max. :1.635e+12	Max. :5.176e+23	Max. :1.283e+24	Max. :3.555e+19	Max. :1.823e+19	Max. :3.091e+20	Max. :25.9777	Max. :60.5935	Max. :617.9170	Max. :16.9693	Max. :19.4099	Max. :76.7622	Max. :0.1386	Max. :0.0017	Max. :0.0176	Max. :0.0066	Max. :0.0076	Max. :0	Max. :140849	Max. :0.9807	Max. :1.0000	Max. :0.9975	Max. :57.058	Max. :23.104	Max. :18.8476	Max. :85505656	Max. :1.717e+15	Max. :9.809e+21	Max. :3.126e+18	Max. :8.0310	Max. :5.9281	Max. :0.7223	Max. :175.3947	Max. :3.525e+11	Max. :2.426e+22	Max. :8.098e+22	Max. :1.825e+18	Max. :1.225e+18	Max. :1.436e+19	Max. :93.9830	Max. :662.4172	Max. :8293.848	Max. :174.6308	Max. :174.1215	Max. :562.7741	Max. :0.7774	Max. :0.0311	Max. :0.5675	Max. :0.0891	Max. :0.0928	Max. :0.0014	Max. :35631.8	Max. :0.9870	Max. :1.0000	Max. :0.9981	Max. :56.374	Max. :22.701	Max. :18.4920	Max. :259.2	Max. :912.68	Max. :8.0000	Max. :308821123	Max. :2.259e+16	Max. :4.715e+23	Max. :4.159e+19	Max. :4.964	Max. :2.782	Max. :0.279	Max. :16.600	Max. :1.252e+12	Max. :3.069e+23	Max. :7.404e+23	Max. :2.404e+19	Max. :1.234e+19	Max. :1.832e+20	Max. :27.950	Max. :87.376	Max. :627.766	Max. :12.672	Max. :11.434	Max. :92.899	Max. :0.117	Max. :0.002	Max. :0.01	Max. :0.004	Max. :0.003	Max. :0	Max. :114085	Max. :0.972	Max. :1.000	Max. :0.997	Max. :56.054	Max. :22.190	Max. :18.573	Max. :72213243	Max. :1.241e+15	Max. :6.053e+21	Max. :2.396e+18	Max. :13.448	Max. :19.719	Max. :2.609	Max. :611.380	Max. :2.923e+11	Max. :1.683e+22	Max. :5.210e+22	Max. :1.399e+18	Max. :1.016e+18	Max. :9.946e+18	Max. :276.318	Max. :2741.695	Max. :63118.30	Max. :466.450	Max. :369.830	Max. :2877.496	Max. :4.533	Max. :0.738	Max. :16.988	Max. :0.98	Max. :0.777	Max. :0.013	Max. :32406	Max. :0.979	Max. :1.000	Max. :0.999	Max. :55.183	Max. :22.311	Max. :18.240	Max. :242.292	Max. :734.9	Max. :6.0000	Max. :243030424	Max. :1.407e+16	Max. :2.311e+23	Max. :2.710e+19	Max. :6.215	Max. :2.838	Max. :0.408	Max. :50.977	Max. :9.772e+11	Max. :1.880e+23	Max. :4.504e+23	Max. :1.585e+19	Max. :8.347e+18	Max. :1.119e+20	Max. :51.132	Max. :191.662	Max. :2122.910	Max. :50.723	Max. :50.553	Max. :182.251	Max. :0.199	Max. :0.004	Max. :0.031	Max. :0.008	Max. :0.008	Max. :0	Max. :91862	Max. :0.967	Max. :1.000	Max. :1.000	Max. :50.403	Max. :22.264	Max. :18.180	Max. :60618204	Max. :8.775e+14	Max. :3.583e+21	Max. :1.761e+18	Max. :14.157	Max. :10.311	Max. :2.323	Max. :1309.539	Max. :2.437e+11	Max. :1.178e+22	Max. :3.483e+22	Max. :1.037e+18	Max. :7.782e+17	Max. :6.932e+18	Max. :327.414	Max. :2492.875	Max. :103155.64	Max. :1303.359	Max. :1299.239	Max. :3605.319	Max. :2.772	Max. :0.035	Max. :7.392	Max. :1.015	Max. :1.012	Max. :0.002	Max. :30286.4	Max. :0.977	Max. :1.000	Max. :1.000	Max. :49.514	Max. :22.358	Max. :17.884	Max. :240.712	Max. :573.448	Max. :6.00	Max. :184531954	Max. :8.164e+15	Max. :1.012e+23	Max. :1.692e+19	Max. :7.021	Max. :3.351	Max. :0.373	Max. :193.305	Max. :7.452e+11	Max. :1.104e+23	Max. :2.633e+23	Max. :1.011e+19	Max. :5.568e+18	Max. :6.424e+19	Max. :84.196	Max. :88.967	Max. :6898.659	Max. :192.202	Max. :191.467	Max. :462.156	Max. :0.354	Max. :0.002	Max. :0.122	Max. :0.052	Max. :0.052	Max. :0	Max. :71681	Max. :0.972	Max. :1.000	Max. :1.000	Max. :41.957	Max. :22.471	Max. :17.579	Max. :49064625	Max. :5.773e+14	Max. :1.892e+21	Max. :1.231e+18	Max. :19.611	Max. :6.290	Max. :0.719	Max. :4345.452	Max. :1.979e+11	Max. :7.841e+21	Max. :2.082e+22	Max. :7.361e+17	Max. :6.431e+17	Max. :4.524e+18	Max. :662.334	Max. :473.929	Max. :427358.8	Max. :4322.121	Max. :4306.567	Max. :10158.080	Max. :7.758	Max. :0.016	Max. :58.633	Max. :5.479	Max. :5.459	Max. :0.016	Max. :30089.0	Max. :0.982	Max. :1.000	Max. :1.000	Max. :41.755	Max. :22.507	Max. :17.352	Max. :238.48	Max. :450.09	Max. :6.0000	Max. :141422172	Max. :4.800e+15	Max. :4.536e+22	Max. :1.039e+19	Max. :12.078	Max. :2.802	Max. :0.231	Max. :93.727	Max. :5.683e+11	Max. :6.499e+22	Max. :1.532e+23	Max. :6.245e+18	Max. :3.483e+18	Max. :3.737e+19	Max. :157.219	Max. :3703.779	Max. :23597.343	Max. :116.724	Max. :132.056	Max. :1460.896	Max. :0.886	Max. :0.118	Max. :0.749	Max. :0.03	Max. :0.031	Max. :0	Max. :56261	Max. :0.973	Max. :1.000	Max. :1.000	Max. :46.014	Max. :22.362	Max. :17.225	Max. :39694362	Max. :3.776e+14	Max. :9.941e+20	Max. :8.397e+17	Max. :17.590	Max. :6.303	Max. :0.983	Max. :274.406	Max. :1.595e+11	Max. :5.145e+21	Max. :1.208e+22	Max. :5.500e+17	Max. :5.643e+17	Max. :2.939e+18	Max. :337.296	Max. :17045.143	Max. :108788.98	Max. :285.981	Max. :293.698	Max. :4567.181	Max. :2.779	Max. :1.157	Max. :7.385	Max. :0.214	Max. :0.22	Max. :0.003	Max. :29809.5	Max. :0.973	Max. :1.000	Max. :1.000	Max. :45.925	Max. :22.399	Max. :17.016	Max. :236.349	Max. :348.256	Max. :7.0000	Max. :103908616	Max. :2.597e+15	Max. :1.792e+22	Max. :5.927e+18	Max. :7.413	Max. :3.909	Max. :0.214	Max. :394.221	Max. :4.166e+11	Max. :3.557e+22	Max. :8.263e+22	Max. :3.604e+18	Max. :2.055e+18	Max. :1.999e+19	Max. :117.872	Max. :109.662	Max. :13570.728	Max. :405.549	Max. :413.101	Max. :611.524	Max. :0.445	Max. :0.002	Max. :0.193	Max. :0.094	Max. :0.096	Max. :0	Max. :43532	Max. :0.972	Max. :1.000	Max. :1.000	Max. :42.733	Max. :22.266	Max. :16.773	Max. :30747979	Max. :2.265e+14	Max. :4.576e+20	Max. :5.324e+17	Max. :8.417	Max. :8.882	Max. :1.425	Max. :246.942	Max. :1.233e+11	Max. :3.127e+21	Max. :7.267e+21	Max. :3.467e+17	Max. :3.558e+17	Max. :1.752e+18	Max. :83.135	Max. :282.797	Max. :6814.175	Max. :257.105	Max. :263.880	Max. :417.867	Max. :0.656	Max. :0.023	Max. :0.387	Max. :0.042	Max. :0.044	Max. :0	Max. :29543.7	Max. :0.967	Max. :1.000	Max. :1.000	Max. :41.685	Max. :22.301	Max. :16.576	Max. :234.397	Max. :316.040	Max. :6.0000	Max. :73090459	Max. :1.261e+15	Max. :5.981e+21	Max. :3.113e+18	Max. :4.457	Max. :2.474	Max. :0.286	Max. :29.382	Max. :2.967e+11	Max. :1.823e+22	Max. :4.243e+22	Max. :1.930e+18	Max. :1.141e+18	Max. :1.002e+19	Max. :26.949	Max. :49.871	Max. :638.793	Max. :17.530	Max. :14.218	Max. :92.492	Max. :0.123	Max. :0.001	Max. :0.014	Max. :0.005	Max. :0.004	Max. :0	Max. :39505	Max. :0.970	Max. :1.000	Max. :1.000	Max. :42.058	Max. :21.703	Max. :16.344	Max. :23214720	Max. :1.215e+14	Max. :1.769e+20	Max. :3.087e+17	Max. :7.025	Max. :6.600	Max. :2.241	Max. :42.231	Max. :9.229e+10	Max. :1.767e+21	Max. :4.120e+21	Max. :2.388e+17	Max. :2.438e+17	Max. :9.872e+17	Max. :56.246	Max. :474.557	Max. :2820.646	Max. :40.636	Max. :39.572	Max. :250.787	Max. :0.532	Max. :0.013	Max. :0.252	Max. :0.011	Max. :0.01	Max. :0	Max. :29299.6	Max. :0.971	Max. :1.000	Max. :1.000	Max. :41.788	Max. :21.761	Max. :16.156	Max. :232.159	Max. :283.688	Max. :4.0000	Max. :48013563	Max. :5.045e+14	Max. :1.345e+21	Max. :2.072e+18	Max. :6.653	Max. :2.235	Max. :0.397	Max. :63.114	Max. :1.992e+11	Max. :8.237e+21	Max. :2.055e+22	Max. :1.195e+18	Max. :7.140e+17	Max. :4.573e+18	Max. :54.351	Max. :115.524	Max. :2704.378	Max. :58.872	Max. :56.045	Max. :304.055	Max. :0.273	Max. :0.003	Max. :0.068	Max. :0.021	Max. :0.02	Max. :0	Max. :35461	Max. :0.972	Max. :0.993	Max. :1.000	Max. :42.217	Max. :21.431	Max. :14.392	Max. :20363317	Max. :5.384e+13	Max. :4.668e+19	Max. :2.211e+17	Max. :10.189	Max. :10.013	Max. :3.744	Max. :116.909	Max. :6.505e+10	Max. :8.788e+20	Max. :2.193e+21	Max. :2.102e+17	Max. :2.170e+17	Max. :6.758e+17	Max. :117.167	Max. :1321.551	Max. :12642.835	Max. :67.944	Max. :54.022	Max. :767.723	Max. :1.550	Max. :0.109	Max. :2.212	Max. :0.043	Max. :0.053	Max. :0.002	Max. :29019.9	Max. :0.973	Max. :0.999	Max. :1.000	Max. :41.755	Max. :21.493	Max. :14.349	Max. :20509.63	Max. :320.010	Max. :0.51469	Max. :1.7125	Max. :0	Max. :14.225	Max. :2.8667	Max. :3.0000	NA	NA	NA	Max. :0.2	Max. :1.9	Max. :1.4	Max. :2	Max. :4.601	Max. : 1.245e-06	Max. :0.635689	Max. :1.2013200	Max. :-0.1770	Max. :16.1506	Max. : 1.078e-05	Max. :0.54304	Max. :5.386880	Max. :-0.1863	Max. :47.9604	Max. : 1.388e-06	Max. :0.430955	Max. :1.6430900	Max. : -0.07612	Max. :13.36110	Max. : 1.299e-06	Max. :0.516563	Max. :4.222430	Max. :-0.1056	Max. :42.3955	Max. :75081554	Max. :14297165	Max. :27645380	Max. :0.9915	Max. : 0.06003	Max. :0.30144	Max. :-0.0006912	Max. :0.36990	Max. :3.068e-01	Max. :0.70524	Max. : 0.05589	Max. :0.30466	Max. : 0.01291	Max. :0.33391	Max. : 0.249921	Max. :0.67106	Max. : 0.12699	Max. :0.30686	Max. : 0.058075	Max. :0.33255	Max. : 0.04754	Max. :0.46476	Max. : 0.04670	Max. :0.17221	Max. : 0.007429	Max. :0.27081	Max. : 0.259225	Max. :0.61432	Max. : 0.09531	Max. :0.39516	Max. :1.886e+77	Max. :9.550e+75	Max. : 0.9943	Max. : 0.06219	Max. :0.30440	Max. :2.5	Max. :7.1	Max. :0.5
	(Other) :14126	(Other):14054	(Other):9859	(Other):11932	(Other): 876	NA’s :14132	NA’s :14132	NA’s :14132	NA’s :14132	NA’s :14132	NA’s :14132	NA’s :14132	NA’s :14132	NA’s :14132	NA’s :14132	NA’s :14132	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA’s :19	NA’s :19	NA’s :19	NA’s :19	NA’s :19	NA’s :19	NA	NA	NA	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA’s :407	NA	NA	NA	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA’s :1015	NA	NA	NA	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA’s :2733	NA	NA	NA	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA’s :4323	NA	NA	NA	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA’s :5688	NA	NA	NA	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA’s :7026	NA	NA	NA	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA’s :8280	NA	NA	NA	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA’s :9341	NA	NA	NA	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA’s :10246	NA	NA	NA	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA’s :11011	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA’s :77	NA’s :77	NA	NA	NA	NA	NA’s :77	NA’s :77	NA	NA	NA	NA	NA’s :77	NA’s :77	NA	NA	NA	NA	NA’s :77	NA’s :77	NA	NA	NA	NA	NA’s :139	NA’s :139	NA’s :139	NA’s :139	NA’s :139	NA’s :77	NA’s :77	NA	NA	NA

---

** Wnioski z analizy podsumowanych danych: **

• pewne wiersze zawierają warości niedostępne (NA),
• niektóre kolumny zawierają stałe wartości,
• różne typy zmiennych,
• niektóre kolumny niosą redundantne informacje,
• pewne zmienne są obliczane na podstawie całego pliku PDB, a nie tylko na podstawie ligandu,
• niektóre kolumny niosą tą samą informację, różniąc się jedynie progiem odcięcia

---

Dalsze czyszczenie danych

Zamiana wartości NA na 0

dane <- dane %>% 
  replace(is.na(.), 0)

Przefiltrowanie istotności zmiennych

---

Po ogólnej analizie danych, można zauważyć, że pewne kolumny zawierają stałe wartości:

• wartość 0 przyjmują: local_BAa, local_NPa, local_Ra, local_RGa, local_SRGa, local_CCSa, local_CCPa, local_ZOa, ocal_ZDa, local_ZD_minus_a, local_ZD_plus_a

• Wartość ‘DELFWT’ przyjmuje: fo_col

• Wartość ‘PHDELWT’ przyjmuje: fc_col

• Wartość 0 przyjmuje: weight_col

• Wartość 0.2 przyjmuje: grid_space

• Wartość 1.9 przyjmuje: solvent_radius

• Wartość 1.4 przyjmuje: solvent_opening_radius

• Wartość 2 przyjmuje: resolution_max_limit

• Wartość 2.5 przyjmuje: part_step_FoFc_std_min

• Wartość 7.1 przyjmuje: part_step_FoFc_std_max

• Wartość 0.5 przyjmuje: part_step_FoFc_std_step

---

Dlatego do dalszej analizy danych, nie bedziemy wykorzystywać wymienionych kolumn

dane <- dane %>%
  select(-(local_BAa:local_ZD_plus_a), -(fo_col:resolution_max_limit), -(part_step_FoFc_std_min:part_step_FoFc_std_step))

---

W zbiorze danych znajdują się kolumny, których wartości są obliczane na podstawie całego pliku PDB, a nie tylko na podstawie ligandu:

• TwoFoFc_mean, TwoFoFc_std, TwoFoFc_square_std, TwoFoFc_min, TwoFoFc_max

• Fo_mean, Fo_std, Fo_square_std, Fo_min, Fo_max

• FoFc_mean, FoFc_std, FoFc_square_std, FoFc_min, FoFc_max

• Fc_mean, Fc_std, Fc_square_std, Fc_min, Fc_max

• resolution

• TwoFoFc_bulk_mean, TwoFoFc_bulk_std, TwoFoFc_void_mean, TwoFoFc_void_std, TwoFoFc_modeled_mean, TwoFoFc_modeled_std

• Fo_bulk_mean, Fo_bulk_std, Fo_void_mean, Fo_void_std, Fo_modeled_mean, Fo_modeled_std

• Fc_bulk_mean, Fc_bulk_std, Fc_void_mean, Fc_void_std, Fc_modeled_mean, Fc_modeled_std

• FoFc_bulk_mean, FoFc_bulk_std, FoFc_void_mean, FoFc_void_std, FoFc_modeled_mean, FoFc_modeled_std

• TwoFoFc_void_fit_binormal_mean1, TwoFoFc_void_fit_binormal_std1, TwoFoFc_void_fit_binormal_mean2, TwoFoFc_void_fit_binormal_std2, TwoFoFc_void_fit_binormal_scale, TwoFoFc_solvent_fit_normal_mean, TwoFoFc_solvent_fit_normal_std

---

Do dalszej analizy, nie bedziemy wykorzystywać kolumn wymienionych wyżej

dane <- dane %>%
  select(-(TwoFoFc_mean:Fc_max), -resolution, -(TwoFoFc_bulk_mean:TwoFoFc_solvent_fit_normal_std))

---

7. Sekcje sprawdzającą korelacje między zmiennymi; sekcja ta powinna zawierać jakąś formę graficznej prezentacji korelacji

---

Poniższe korelogramy reprezentują korelację między poszczególnymi zmiennymi. Zbiór wszystkich kolumn, dla analizowanych danych, jest bardzo duży, dlatego po wnikliwej analizie, można było zauważyć, że kolumny part_XX niosą tą samą informację, jednak dla innego progu odcięcia. Dlatego, aby ograniczyć zbiór porównywanych zmiennych, kolumny podzielone zostały na zbiór zmiennych ogólnych (nie zawierających kolumn part), a także na rozłączne zbiory part. Korelacja nie była liczona dla dwóch różnych zbiorów part (np. między part01 i part02), a jedynie wewnątrz poszczególnych zbiorów part i atrybutów ogólnych.

Przygotowanie danych

---

Filtrowanie zbioru danych i eliminacja kolumn zawierających wartości stałe i tekstowe ***

# Dane bez stałych wartości i wartości tekstowych
dane_obliczenia <- dane %>%
  select(-(title:chain_id), -local_parts, -local_min, -part_00_blob_parts) 

Korelogram zmiennych ogólnych

---

Są to wszystkie zmienne ogólne (nie zawierają kolumn part_XX_) ***

corrgram(dane_obliczenia %>%
            select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)), 
         order=TRUE, 
         main="Korelogram dla podstawowych atrybutów", 
         lower.panel=panel.shade, 
         upper.panel=panel.pie, 
         diag.panel=panel.minmax, 
         text.panel=panel.txt)

Korelogram zmiennych ogólnych i part_00

---

Są to wszystkie zmienne ogólne i kolumny z part_00 ***

# Dane dla part_00
corrgram(data.frame(dane_obliczenia %>%
                                select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)), 
                    dane_obliczenia %>%
                                select(part_00_blob_electron_sum:part_00_density_sqrt_E3)), 
         order=TRUE, 
         main="Korelogram dla atrybutów wraz z part_00", 
         lower.panel=panel.shade, 
         upper.panel=panel.pie, 
         diag.panel=panel.minmax, 
         text.panel=panel.txt)

Korelogram zmiennych ogólnych i part_01

---

Są to wszystkie zmienne ogólne i kolumny z part_01 ***

# Dane dla part_01
corrgram(data.frame(dane_obliczenia %>%
                                select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)), 
                    dane_obliczenia %>%
                                select(part_01_blob_electron_sum:part_01_density_sqrt_E3)), 
         order=TRUE, 
         main="Korelogram dla atrybutów wraz z part_01", 
         lower.panel=panel.shade, 
         upper.panel=panel.pie, 
         diag.panel=panel.minmax, 
         text.panel=panel.txt)

Korelogram zmiennych ogólnych i part_02

---

Są to wszystkie zmienne ogólne i kolumny z part_02 ***

# Dane dla part_02
corrgram(data.frame(dane_obliczenia %>%
                                select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)), 
                    dane_obliczenia %>%
                                select(part_02_blob_electron_sum:part_02_density_sqrt_E3)), 
         order=TRUE, 
         main="Korelogram dla atrybutów wraz z part_02", 
         lower.panel=panel.shade, 
         upper.panel=panel.pie, 
         diag.panel=panel.minmax, 
         text.panel=panel.txt)

Korelogram zmiennych ogólnych i part_03

---

Są to wszystkie zmienne ogólne i kolumny z part_03 ***

# Dane dla part_03
corrgram(data.frame(dane_obliczenia %>%
                                select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)), 
                    dane_obliczenia %>%
                                select(part_03_blob_electron_sum:part_03_density_sqrt_E3)), 
         order=TRUE, 
         main="Korelogram dla atrybutów wraz z part_03", 
         lower.panel=panel.shade, 
         upper.panel=panel.pie, 
         diag.panel=panel.minmax, 
         text.panel=panel.txt)

Korelogram zmiennych ogólnych i part_04

---

Są to wszystkie zmienne ogólne i kolumny z part_04 ***

# Dane dla part_04
corrgram(data.frame(dane_obliczenia %>%
                                select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)), 
                    dane_obliczenia %>%
                                select(part_04_blob_electron_sum:part_04_density_sqrt_E3)), 
         order=TRUE, 
         main="Korelogram dla atrybutów wraz z part_04", 
         lower.panel=panel.shade, 
         upper.panel=panel.pie, 
         diag.panel=panel.minmax, 
         text.panel=panel.txt)

Korelogram zmiennych ogólnych i part_05

---

Są to wszystkie zmienne ogólne i kolumny z part_05 ***

# Dane dla part_05
corrgram(data.frame(dane_obliczenia %>%
                                select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)), 
                    dane_obliczenia %>%
                                select(part_05_blob_electron_sum:part_05_density_sqrt_E3)), 
         order=TRUE, 
         main="Korelogram dla atrybutów wraz z part_05", 
         lower.panel=panel.shade, 
         upper.panel=panel.pie, 
         diag.panel=panel.minmax, 
         text.panel=panel.txt)

Korelogram zmiennych ogólnych i part_06

---

Są to wszystkie zmienne ogólne i kolumny z part_06 ***

# Dane dla part_06
corrgram(data.frame(dane_obliczenia %>%
                                select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)), 
                    dane_obliczenia %>%
                                select(part_06_blob_electron_sum:part_06_density_sqrt_E3)), 
         order=TRUE, 
         main="Korelogram dla atrybutów wraz z part_06", 
         lower.panel=panel.shade, 
         upper.panel=panel.pie, 
         diag.panel=panel.minmax, 
         text.panel=panel.txt)

Korelogram zmiennych ogólnych i part_07

---

Są to wszystkie zmienne ogólne i kolumny z part_07 ***

# Dane dla part_07
corrgram(data.frame(dane_obliczenia %>%
                                select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)), 
                    dane_obliczenia %>%
                                select(part_07_blob_electron_sum:part_07_density_sqrt_E3)), 
         order=TRUE, 
         main="Korelogram dla atrybutów wraz z part_07", 
         lower.panel=panel.shade, 
         upper.panel=panel.pie, 
         diag.panel=panel.minmax, 
         text.panel=panel.txt)

Korelogram zmiennych ogólnych i part_08

---

Są to wszystkie zmienne ogólne i kolumny z part_08 ***

# Dane dla part_08
corrgram(data.frame(dane_obliczenia %>%
                                select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)), 
                    dane_obliczenia %>%
                                select(part_08_blob_electron_sum:part_08_density_sqrt_E3)), 
         order=TRUE, 
         main="Korelogram dla atrybutów wraz z part_08", 
         lower.panel=panel.shade, 
         upper.panel=panel.pie, 
         diag.panel=panel.minmax, 
         text.panel=panel.txt)

Korelogram zmiennych ogólnych i part_09

---

Są to wszystkie zmienne ogólne i kolumny z part_09 ***

# Dane dla part_09
corrgram(data.frame(dane_obliczenia %>%
                                select(-(part_00_blob_electron_sum:part_09_density_sqrt_E3)), 
                    dane_obliczenia %>%
                                select(part_09_blob_electron_sum:part_09_density_sqrt_E3)), 
         order=TRUE, 
         main="Korelogram dla atrybutów wraz z part_09", 
         lower.panel=panel.shade, 
         upper.panel=panel.pie, 
         diag.panel=panel.minmax, 
         text.panel=panel.txt)

8. Określenie ile przykładów ma każda z klas res_name

podsumowanie <- dane %>%
  group_by(res_name) %>%
  summarise(n=n()) #Podliczenie

Liczba wszystkich klas res_name wynosi: ** 2685 **

Liczba ta jest bardzo duża, dlatego na poniższym wykresie przedstawione zostało 20 najliczniejszych klas.

Wykres 20 najliczniejszych klasy

podsumowanie <- podsumowanie %>%
  arrange(desc(n)) %>%
  slice(1:20)

podsumowanie$res_name <- factor(podsumowanie$res_name, levels=unique(podsumowanie$res_name))

ggplot(podsumowanie , aes(x = res_name, y = n, order = desc(n))) + 
  geom_bar(stat="identity") + 
  theme_bw()

9. Wykresy rozkładów liczby atomów (local_res_atom_non_h_count) i elektronów (local_res_atom_non_h_electron_sum)

rozk <- stack(dane %>% select(local_res_atom_non_h_count, local_res_atom_non_h_electron_sum))

ggplot(rozk, aes(x = values)) + geom_density(aes(group=ind, colour=ind, fill=ind), alpha=0.3)

---

Analizując powyższy wykres, można zauważyć, że:

• rozkład liczby atomów jest bardziej skoncentrowany i mieści się w przedziale od 1 do 104. Alanizując natomiast pierwszy (1) i trzeci (20) kwartyl widzimy, że liczba atomów jest o wiele bardziej skupiona prz wartościach minimalnych. Mediana i średnia dla zmiennej local_res_atom_non_h_count wynosza odpowiednio 7 i 13.43
• liczba elektronów obejmuje przedział od 3 do 800. Wartości local_res_atom_non_h_electron_sum są bardziej rozrzucone. Jednak analizując pierwszy (30) i trzeci (145) kwartyl widzimy, że wartości te są skoncetrowane bliżej średniej (101.5) i mediany (55), dlatego też na wykresie widać długi “ogon”

10. Próbę odtworzenia następującego wykresu (oś X - liczba elektronów, oś y - liczba atomów)

d <- dane %>%
  mutate(y = 6+(round(jitter(local_res_atom_non_h_count, factor = 2.5, amount = 0))), x = 50+(round(jitter(local_res_atom_non_h_electron_sum, factor = 3.5, amount = 0)))) %>% select(x, y)

rozklad <- ggplot(d, 
  aes(x = x, 
      y = y)) +
  stat_density2d(geom="tile", aes(fill = ..density..), contour = FALSE) + #, position = position_jitter()) + 
  scale_fill_gradientn(colours=rev(brewer.pal(11, "Spectral"))) +
  coord_cartesian(xlim = c(0,650), 
                  ylim = c(0,100)) +
  scale_x_continuous(breaks = seq(0,600,100)) +
  scale_y_continuous(breaks = seq(0,100,20)) +
  theme(legend.position="none", 
        axis.title.x = element_blank(), 
        axis.title.y = element_blank(),
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        panel.background = element_rect(fill = rgb(0.368, 0.309, 0.635))) 

#Wykresy margin
ggExtra::ggMarginal(
  rozklad,
  type = 'histogram',
  margins = 'both',
  size = 5,
  xparams = list(binwidth = 6, colour = "black", fill = "red"),
  yparams = list(binwidth = 1, colour = "black", fill = "red")
)

Do stworzenia wykresu, wykozystana została biblioteka ggplot2 i ggEkstra. Druga wymieniona biblioteka umożliwiła stowrzenie wykresów na poszczególnych osiach. Do pokolorowania rozkładu wykorzystana została biblioteka RColorBrewer, z której skorzystano z typu kolorów Spectral. Należało go jednak odwrócić. Aby dokładniej przeanalizowac dane, które były skupione blisko wartości zerowych, wykorzystany został jitter.

11. Tabelę pokazującą 10 klas z największą niezgodnością liczby atomów (local_res_atom_non_h_count vs dict_atom_non_h_count) i tabelę pokazującą 10 klas z największą niezgodnością liczby elektronów (local_res_atom_non_h_electron_sum vs dict_atom_non_h_electron_sum;)

Niezgodność liczby atomów

kable(dane %>%
  mutate(blad = abs(dict_atom_non_h_count - local_res_atom_non_h_count)) %>%
  select(res_name, blad) %>%
  group_by(res_name) %>%
  summarise(minimum = min(blad),
            maximum = max(blad),
            warjancja = var(blad),
            odch_stand = sd(blad)) %>%
  arrange(desc(maximum)) %>%
  slice(1:10))

res_name	minimum	maximum	warjancja	odch_stand
PEU	64	69	12.5000	3.535534
CDL	0	60	527.6944	22.971601
PC1	15	43	252.3333	15.885003
3TL	0	33	363.0000	19.052559
CPQ	33	33	NA	NaN
JEF	33	33	NA	NaN
PE3	0	33	544.5000	23.334524
PEE	2	33	267.3333	16.350331
VV7	33	33	NA	NaN
LI1	27	32	12.5000	3.535534

Niezgodność liczby elektronów

kable(dane %>%
  mutate(blad = abs(dict_atom_non_h_electron_sum - local_res_atom_non_h_electron_sum)) %>%
  select(res_name, blad) %>%
  group_by(res_name) %>%
  summarise(minimum = min(blad),
            maximum = max(blad),
            warjancja = var(blad),
            odch_stand = sd(blad)) %>%
  arrange(desc(maximum)) %>%
  slice(1:10))

res_name	minimum	maximum	warjancja	odch_stand
PEU	426	460	578.00	24.04163
CDL	0	360	19013.11	137.88804
PC1	91	284	11064.33	105.18713
ATG	252	252	0.00	0.00000
SAP	252	252	NA	NaN
ABG	242	242	NA	NaN
CPQ	225	225	NA	NaN
PEE	12	224	12761.33	112.96607
VV7	224	224	NA	NaN
PE3	0	222	24642.00	156.97771

12. Sekcję pokazującą rozkład wartości wszystkich kolumn zaczynających się od part_01 z zaznaczeniem (graficznym i liczbowym) średniej wartości.

part <- dane %>% select(part_01_blob_electron_sum:part_01_density_sqrt_E3)

for(i in seq_along(part)) {
  
  srednia <- mean(part[,i], rm.na=TRUE)
  min <- min(part[,i], rm.na=TRUE)
  max <- max(part[,i], rm.na=TRUE)
  
  w <- ggplot(data = part, aes(x=part[,i], label = srednia)) + 
    scale_x_continuous(colnames(part %>% select(i))) +
    scale_y_continuous("Liczność") +
    geom_histogram(aes(y = ..density.., fill = ..count..)) + geom_density(colour = "red", size = 1.5) +
    scale_fill_gradientn(colours=brewer.pal(11, "Spectral")) +
    geom_vline(data = NULL, aes(xintercept=srednia), linetype = "dashed", size=2) +
    ggtitle(paste("Rozkład wartości dla atrybutu ", colnames(part %>% select(i)), " średnia=", srednia, sep = "")) 
  
  print(w)
}

Powyższe wykresy pokazują rozkład poszczególnych zmiennych dla Part_01. Patrząc na wcześniejsze podsumowanie surowych danych, można zauważyć, że kolumny te posiadały bardzo dużą liczbę wartości pustych, które w dalszej części zostały także zamienione na wartość 0 (stąd też widoczne wysokie rozkłady wartości w okolicach 0)

13. Sekcję sprawdzającą czy na podstawie wartości innych kolumn można przewidzieć liczbę elektronów i atomów oraz z jaką dokładnością można dokonać takiej predykcji; trafność regresji powinna zostać oszacowana na podstawie miar R^2 i RMSE;

Przewidywanie liczby atomów

---

Do przewidywania liczby atomów wykorzystany został mogel regresji liniowej. Dodatkowo, porównany został sposób uczenia modelu regresji z wykorzystaniem standardowego próbkowania i walidacji krzyżowej. Analizując dalsze wyniki, oba podejścia nie zmieniły uzyskanych wyników. Do analizy atrybutów wykorzystane zostały wszystkie zmienna oprócz zmiennych słownikowych (dist_)

---

Budowanie modelu

proba <- dane %>% select(-(title:chain_id), -(dict_atom_non_h_count:dict_atom_S_count))

inTraining <- createDataPartition(y=proba$local_res_atom_non_h_count, 
                                  p= .75, 
                                  list = FALSE)

training <- proba[ inTraining,]
testing <- proba[ -inTraining,]

Dla podstawowego modelu "lm"

lmFit <- train(local_res_atom_non_h_count ~ . , 
               method="lm", 
               data = training)

Dla modelu "lm" z Cross-Validation

ctrl <- trainControl(method = "cv", number = 10)

lmCVFit <- train(local_res_atom_non_h_count ~ . , 
                 method="lm", 
                 data = training, 
                 trControl = ctrl)

Lista najbardziej wartościowych atrybutów

Dla podstawowego modelu "lm"

plot(varImp(lmFit), top = 20)

Dla modelu "lm" z Cross-Validation

plot(varImp(lmCVFit), top = 20)

Trafność regresji

---

Miara RMSE i R^2 dla podstawowego modelu "lm"

predVal<-predict(lmFit, testing)

## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading

modelvalues<-data.frame(obs = testing$local_res_atom_non_h_count, pred=predVal)

defaultSummary(modelvalues)

##      RMSE  Rsquared 
## 1.4232369 0.9904637

Miara RMSE i R^2dla modelu "lm" z Cross-Validation

predVal<-predict(lmCVFit, testing)

## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading

modelvalues<-data.frame(obs = testing$local_res_atom_non_h_count, pred=predVal)

defaultSummary(modelvalues)

##      RMSE  Rsquared 
## 1.4232369 0.9904637

---

Wykres predykcji wartości

Dla podstawowego modelu "lm"

predictedValues <- predict(lmFit)

## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading

plot(training$local_res_atom_non_h_count, predictedValues)

Dla modelu "lm" z Cross-Validation

predictedValues <- predict(lmCVFit)

## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading

plot(training$local_res_atom_non_h_count, predictedValues)

---

---

Przewidywanie liczby elektronów

---

Budowanie modelu

proba <- dane %>% select(-(title:chain_id), -(dict_atom_non_h_count:dict_atom_S_count))

inTraining <- createDataPartition(y=proba$local_res_atom_non_h_electron_sum, 
                                  p= .75, 
                                  list = FALSE)

training <- proba[ inTraining,]
testing <- proba[ -inTraining,]

Dla podstawowego modelu "lm"

lmFit <- train(local_res_atom_non_h_electron_sum ~ . , 
               method="lm", 
               data = training)

Dla modelu "lm" z Cross-Validation

ctrl <- trainControl(method = "cv", number = 10)

lmCVFit <- train(local_res_atom_non_h_electron_sum ~ . , 
                 method="lm", 
                 data = training, 
                 trControl = ctrl)

Lista najbardziej wartościowych atrybutów

Dla podstawowego modelu "lm"

plot(varImp(lmFit), top = 20)

Dla modelu "lm" z Cross-Validation

plot(varImp(lmCVFit), top = 20)

Trafność regresji

---

Miara RMSE i R^2 dla podstawowego modelu "lm"

predVal<-predict(lmFit, testing)

## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading

modelvalues<-data.frame(obs = testing$local_res_atom_non_h_electron_sum, pred=predVal)

defaultSummary(modelvalues)

##        RMSE    Rsquared 
## 120.1233936   0.4140984

Miara RMSE i R^2dla modelu "lm" z Cross-Validation

predVal<-predict(lmCVFit, testing)

## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading

modelvalues<-data.frame(obs = testing$local_res_atom_non_h_electron_sum, pred=predVal)

defaultSummary(modelvalues)

##        RMSE    Rsquared 
## 120.1233936   0.4140984

---

Wykres predykcji wartości

Dla podstawowego modelu "lm"

predictedValues <- predict(lmFit)

## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading

plot(training$local_res_atom_non_h_electron_sum, predictedValues)

Dla modelu "lm" z Cross-Validation

predictedValues <- predict(lmCVFit)

## Warning in predict.lm(modelFit, newdata): prediction from a rank-deficient
## fit may be misleading

plot(training$local_res_atom_non_h_electron_sum, predictedValues)

---

---

14. Sekcję próbującą stworzyć klasyfikator przewidujący wartość atrybutu res_name (w tej sekcji należy wykorzystać wiedzę z pozostałych punktów oraz wykonać dodatkowe czynności, które mogą poprawić trafność klasyfikacji); trafność klasyfikacji powinna zostać oszacowana na danych inne niż uczące za pomocą mechanizmu (stratyfikowanej!) oceny krzyżowej lub (stratyfikowanego!) zbioru testowego.

Do stworzenia klasyfikatora dla zmiennej res_name, wykorzystano wiedzę uzyskaną z powyższych badań. Znając dużą liczbę różnych wartości dla zmiennej res_name, klasyfikator został wykorzystany do zbudowania modelu dla wartości zmiennych przekraczających liczbę 50. Tym samym ograniczyło to liczbę analizowanego zbioru. Kolejnym wnioskiem wywinioskowanym z eksploracji danych było dostrzeżenie korelacji między zmiennymi. Powyższe korelogramy pokazały, że niektóre kolumny nie są ze sobą powiązane i tym samym niepotrzebne jest porównywanie ich do stworzenia modelu. Dlatego ustawiony został próg korelacji 0.90. Do budowy klasyfikacji wykorzystane zostały dwa modele - jeden z nich opierał się na algorytmie GBM, a drugi na Random Forest. Analizując poszczególne wyniki, algorytm Random Forest stworzył model, który okazał się bardziej trafnym (wyższy wskażnik Kappa i Accuracy). Analizując także Confussion Matrix, można dostrzec, że klasyfikator popełniał wysoki błąd dla niektórych ligardów.

Przygotowanie danych do klasyfikacji

da <- dane %>%
  group_by(res_name) %>%
  summarise(liczba = n()) %>%
  filter(liczba > 50) 

#res_name <- dane %>%
#  filter(res_name %in% da$res_name) %>%
#  select(res_name) 

descr <- dane %>%
  filter(res_name %in% da$res_name) %>%
  select(-(res_id:dict_atom_S_count), -local_min, -title, -pdb_code) 

cols = 2:length(descr)
descr[,cols] = apply(descr[,cols], 2, function(x) as.numeric(x))

#res_name <- res_name$res_name

predictors <- names(descr)[names(descr) != "res_name"]

inTrain <- createDataPartition(descr$res_name, p = 3/4, list = FALSE)

training <- descr[inTrain,]
testing <- descr[-inTrain,]

Filtrowanie zmiennych

res_name_training <- training %>% select(res_name)
res_name_testing<- testing %>% select(res_name)

training <- training %>% select(-res_name)
testing <- testing %>% select(-res_name)

ncol(training)

## [1] 701

descrCorr <- cor(training)

highCorr <- findCorrelation(descrCorr, 0.90)


training <- training[, -highCorr]
testing <- testing[, -highCorr]

ncol(training)

## [1] 159

res_name_training <- res_name_training %>% mutate(name = paste("name_", res_name, sep="")) %>% select(name)
res_name_testing <- res_name_testing %>% mutate(name = paste("name_", res_name, sep="")) %>% select(name)

Pre-Processing

xTrans <- preProcess(training)
training <- predict(xTrans, training)
testing <- predict(xTrans, testing)

training <- data.frame(res_name_training, training)
testing <- data.frame(res_name_testing, testing)

Zmienne kontrolne

gbmGrid <- expand.grid(interaction.depth = c(1, 5, 10), 
                       n.trees = (5:10)*2, 
                       shrinkage = 0.1,
                       n.minobsinnode = 20)


ctrl <- trainControl(method = "repeatedcv", 
                     number = 2,
                     repeats = 5)

rfGrid <- expand.grid(mtry = c(10:15))

Uczenie klasyfikatora

---

Dla algorytmu GBM

gbmFit <- train(name~., 
                data = training,
                method = "gbm", 
                trControl = ctrl, 
                verbose = FALSE,
                bag.fraction = 0.5, 
                tuneGrid = gbmGrid)

Dla algorytmu Random Forest

rfFit <- train(name~., 
                data = training,
                method = "rf", 
                trControl = ctrl,  
                tuneGrid = rfGrid,
                ntree = 30)

Wykresy podsumowania

Dla algorytmu GBM

plot(gbmFit)

plot(gbmFit, metric = "Kappa")

plot(gbmFit, plotType = "level")

resampleHist(gbmFit)

Dla algorytmu Random Forest

plot(rfFit)

plot(rfFit, metric = "Kappa")

resampleHist(rfFit)

Podsumowanie predykcji modeli

Dla algorytmu GBM

gbmPred <- predict(gbmFit, testing)

matrix <- as.matrix(confusionMatrix(gbmPred, testing$name))

kable(matrix, digits = 3, caption = "Confusion Matrix dla GBM")

Confusion Matrix dla GBM

	name_ACT	name_ADP	name_ATP	name_BME	name_CA	name_CD	name_CIT	name_CL	name_CO	name_CU	name_EDO	name_EPE	name_FAD	name_FE	name_FE2	name_FMN	name_GDP	name_GOL	name_HEM	name_K	name_MAN	name_MES	name_MG	name_MN	name_MPD	name_NA	name_NAD	name_NAG	name_NAP	name_NDP	name_NI	name_PEG	name_PG4	name_PLP	name_PO4	name_SAH	name_SEP	name_SO4	name_TRS	name_ZN
name_ACT	14	0	0	0	0	0	0	3	0	0	2	2	0	0	0	0	0	6	0	0	0	0	3	0	0	1	0	1	0	0	0	1	0	0	2	0	0	6	0	2
name_ADP	0	12	8	0	0	0	0	0	0	0	0	0	1	0	0	2	6	0	0	0	0	0	0	2	0	0	4	0	0	2	1	1	0	0	0	2	0	1	0	0
name_ATP	0	5	8	0	2	0	0	0	0	0	0	0	0	1	0	0	4	0	0	0	0	0	1	2	0	0	1	0	0	0	0	0	0	0	0	2	0	0	1	0
name_BME	0	0	0	3	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
name_CA	0	1	4	2	82	0	0	4	0	4	0	1	0	6	2	0	0	8	1	3	1	1	11	8	0	3	1	0	3	0	1	0	0	0	6	0	1	11	1	15
name_CD	0	0	0	0	0	8	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1
name_CIT	0	0	0	0	1	0	3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
name_CL	6	0	0	0	4	1	0	52	0	0	3	2	0	0	0	0	0	8	0	11	0	0	4	0	0	6	0	0	0	0	1	1	0	0	5	0	0	15	0	3
name_CO	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	1	1	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
name_CU	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	2
name_EDO	0	0	0	3	4	0	0	1	0	0	10	0	0	0	0	0	0	14	0	1	0	0	2	1	1	2	0	0	0	0	0	1	1	1	1	0	0	5	0	0
name_EPE	0	0	0	0	0	0	0	0	0	0	1	4	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	0	0
name_FAD	0	2	0	0	0	0	0	0	0	0	0	0	25	0	0	0	1	0	0	0	0	0	0	1	0	0	1	3	4	1	0	0	0	0	0	0	0	0	0	0
name_FE	0	0	0	0	1	0	0	0	1	0	0	0	1	2	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	7
name_FE2	0	0	0	0	0	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1
name_FMN	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	10	0	0	1	0	0	0	0	0	0	0	0	2	1	0	1	0	1	0	0	2	0	0	0	1
name_GDP	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
name_GOL	9	3	0	4	7	0	5	6	1	0	38	2	1	1	0	0	0	119	1	1	5	3	12	0	10	16	1	13	0	1	1	11	3	0	4	0	1	11	6	3
name_HEM	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	62	0	0	0	2	0	0	0	2	1	0	0	0	0	0	1	0	1	0	0	0	3
name_K	0	0	0	0	1	0	0	5	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	1	0	0
name_MAN	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	1	0	0	0	0	1	0	0	0	0	0	2	0	0	1	0	0	2	0	1
name_MES	0	1	0	0	1	0	1	1	0	0	0	1	0	0	0	0	0	1	0	0	0	2	0	0	0	1	0	2	0	0	0	0	0	0	0	0	0	0	0	0
name_MG	0	0	2	1	17	1	1	3	0	0	4	1	1	0	1	0	1	7	0	2	4	0	32	4	0	8	0	6	0	0	0	0	0	0	2	0	3	8	2	10
name_MN	0	0	0	0	2	0	0	0	1	1	0	0	0	2	2	1	0	0	0	0	0	0	2	3	0	0	0	0	1	1	2	0	0	0	0	0	0	0	0	8
name_MPD	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
name_NA	3	0	0	0	5	0	0	5	0	0	4	0	0	1	0	0	0	4	0	3	0	0	2	0	0	4	0	2	0	1	0	1	0	0	1	0	0	1	0	0
name_NAD	0	2	0	1	0	0	0	0	0	1	0	0	2	0	0	0	0	0	1	0	0	0	0	0	0	0	6	0	2	1	0	0	0	0	0	0	0	0	0	0
name_NAG	0	2	1	1	0	0	2	0	0	0	0	0	1	0	0	2	0	8	1	0	4	1	7	0	0	1	1	62	2	1	0	0	5	0	1	1	1	2	2	0
name_NAP	0	2	0	0	0	0	0	0	0	0	0	0	4	0	0	3	1	0	0	0	0	0	0	1	0	0	1	3	7	6	0	0	0	0	0	0	0	0	0	0
name_NDP	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	2	0	1	0	0	0	0	0	0	0	0	0	0	0
name_NI	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
name_PEG	1	0	0	1	0	0	1	0	0	0	1	0	0	0	0	0	0	2	0	0	0	1	0	0	0	1	0	1	0	0	0	3	1	0	0	0	0	0	0	0
name_PG4	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	1	0	0	1	1	0	0	1	0	1	0	0	0	1	3	0	0	0	0	0	0	0
name_PLP	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	10	0	0	0	0	0	0
name_PO4	0	0	0	0	1	0	0	1	1	4	1	0	0	0	0	0	0	1	0	1	0	1	2	3	1	1	0	0	0	0	0	0	0	0	8	0	0	13	0	5
name_SAH	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	5	0	0	0	0
name_SEP	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	5	0	0	0
name_SO4	6	2	0	0	24	2	1	19	2	1	4	3	0	3	0	0	0	11	0	4	0	6	14	1	0	9	0	0	0	1	3	0	1	0	37	0	1	212	1	16
name_TRS	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2	0	0	0	0	2	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	1	0
name_ZN	1	0	1	0	8	8	0	0	7	14	0	0	0	11	7	0	0	0	3	0	0	0	2	16	0	0	0	0	0	0	6	0	0	1	7	0	1	7	0	134

Dla algorytmu Random Forest

rfPred <- predict(rfFit, testing)

matrix <- as.matrix(confusionMatrix(rfPred, testing$name))

kable(matrix, digits = 3, caption = "Confusion Matrix dla Random Forest")

Confusion Matrix dla Random Forest

	name_ACT	name_ADP	name_ATP	name_BME	name_CA	name_CD	name_CIT	name_CL	name_CO	name_CU	name_EDO	name_EPE	name_FAD	name_FE	name_FE2	name_FMN	name_GDP	name_GOL	name_HEM	name_K	name_MAN	name_MES	name_MG	name_MN	name_MPD	name_NA	name_NAD	name_NAG	name_NAP	name_NDP	name_NI	name_PEG	name_PG4	name_PLP	name_PO4	name_SAH	name_SEP	name_SO4	name_TRS	name_ZN
name_ACT	9	0	0	0	0	0	0	2	0	0	1	1	0	0	0	0	0	3	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	1	0	0	4	0	0
name_ADP	0	11	7	0	0	0	0	0	0	0	0	0	1	1	0	1	9	0	0	0	0	0	1	2	0	0	4	0	0	3	0	0	0	0	0	1	0	0	0	0
name_ATP	0	7	8	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	2	0	0	1	0	0	0	0	0	0	0	0	4	0	0	0	0
name_BME	0	0	0	2	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
name_CA	1	1	2	1	80	1	1	3	0	5	0	1	0	7	2	0	0	2	1	1	0	0	7	8	0	3	0	0	1	0	1	0	0	0	4	0	1	7	0	16
name_CD	0	0	0	0	0	7	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
name_CIT	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	1
name_CL	7	0	0	0	6	1	0	47	1	0	3	0	0	1	0	0	0	6	0	12	2	0	3	0	0	7	0	0	0	0	0	1	0	0	3	0	0	14	0	2
name_CO	0	0	0	0	0	0	0	0	2	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
name_CU	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2
name_EDO	3	0	0	1	1	0	0	0	0	0	12	0	0	0	0	0	0	15	0	1	1	0	3	1	0	1	0	0	0	0	0	3	0	0	1	0	0	5	0	0
name_EPE	0	0	0	0	1	0	0	0	0	0	0	3	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	2	0	0	0	0	0	0	0	0	0	0	0	0
name_FAD	0	2	0	0	0	0	0	0	0	1	0	0	31	0	0	0	2	0	1	0	0	0	1	0	0	0	1	1	5	1	0	0	0	0	0	0	0	0	0	1
name_FE	1	0	0	0	0	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	4
name_FE2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
name_FMN	0	0	0	0	0	0	0	0	0	0	0	0	2	0	0	9	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0
name_GDP	0	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
name_GOL	8	3	0	11	11	0	6	16	1	0	45	4	2	1	0	0	0	136	0	6	4	4	22	0	11	21	0	11	0	0	2	13	8	0	7	0	2	15	7	8
name_HEM	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	65	0	0	0	0	1	0	0	0	2	0	0	0	0	0	1	0	0	0	0	0	2
name_K	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
name_MAN	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
name_MES	0	0	0	0	2	0	0	1	0	0	0	3	0	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
name_MG	0	2	2	0	11	1	2	5	0	0	1	1	0	0	0	0	0	6	0	1	2	0	32	3	1	6	1	2	1	1	0	0	0	0	2	0	1	9	4	4
name_MN	0	1	0	0	3	1	0	0	2	0	0	0	0	0	1	0	0	0	1	0	0	0	3	5	0	2	0	0	1	0	1	0	0	0	1	0	0	0	0	2
name_MPD	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
name_NA	3	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	0	2	0	0	0	0	1	0	0	5	0	0	0	1	0	0	0	0	0	0	0	1	0	1
name_NAD	0	1	0	0	0	0	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	1	0	0	0	10	0	1	0	0	0	0	0	0	0	0	0	0	1
name_NAG	0	3	4	0	3	0	1	0	0	0	1	0	0	0	0	3	0	8	2	0	3	1	6	1	0	2	0	71	2	1	0	2	1	0	1	2	2	3	2	1
name_NAP	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	3	0	0	0	0	0	0	0	1	0	0	1	2	6	6	0	0	0	0	0	0	0	0	0	0
name_NDP	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	2	0	0	0	0	0	0	0	0	0	0	2
name_NI	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1
name_PEG	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	2	0	0	0	2	0	0	0	1	0	1	0	0	0	2	3	0	0	0	0	0	0	0
name_PG4	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	1	2	0	0	0	0	0	0	1	0	1	0	0	0	1	4	0	0	0	0	0	0	0
name_PLP	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	1	0	1	2	0	0	0	0	0	0	0	0	0	0	10	1	1	1	0	0	1
name_PO4	2	0	0	0	4	1	0	2	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	1	1	0	0	0	0	0	0	0	0	4	0	0	6	0	1
name_SAH	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	5	0	0	0	0
name_SEP	0	0	0	0	3	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	3	0	0	0
name_SO4	6	0	0	0	26	2	0	21	2	2	3	2	1	1	0	0	0	12	0	6	0	7	14	2	0	6	0	2	0	1	3	0	0	0	44	0	3	224	1	14
name_TRS	0	0	0	0	0	0	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
name_ZN	0	1	1	0	10	6	0	0	7	17	0	0	0	16	7	1	0	0	2	0	0	0	2	16	0	0	1	0	1	0	9	0	0	2	5	0	2	7	0	148